Artykuły w czasopismach na temat „Entity Grid Model”

Clustering-based reactive voltage partitioning is successful in reducing grid cascading faults, by using clustering methods to categorize different power-consuming entities in the power grid into distinct regions. In reality, each power-consuming entity has different electrical characteristics. Additionally, due to the irregular and uneven distribution of the population, the distribution of electricity consumption is also irregular and uneven. However, the existing method neglects the electrical difference among each entity and the irregular and uneven density distribution of electricity consumption, resulting in poor accuracy and adaptability of these methods. To address these problems, an enhanced density peak model-based power grid reactive voltage partitioning method is proposed in this paper, called EDPVP. First, the power grid is modeled as a weighted reactive network to consider entity electrical differences. Second, the novel local density and density following distance are designed to enhance the density peak model to address the problem that the traditional density peak model cannot adapt to weighted networks. Finally, the enhanced density peak model is further equipped with an optimized cluster centers selection strategy and an updated remaining node assignment strategy, to better identify irregular and uneven density distribution of electricity consumption, and to achieve fast and accurate reactive voltage partition. Experiments on two real power grids demonstrate the effectiveness of the EDPVP.

Based on video grid structure, A Video Grid model was established using AnyLogic simulation software. First, the components required for modeling functions are introduced, and then create a model entity, video service processes, animation and analysis chart. Simulation results show that the AnyLogic-based video grid model, the model parameters can be adjusted, not only from the macroscopic dynamic observation of the entire system dynamics, but also from the microscopic measurement of the specified components. The construction of the actual video grid can provide a decision support.

The KBQA (Knowledge-Based Question Answering) system is an essential part of the smart customer service system. KBQA is a type of QA (Question Answering) system based on KB (Knowledge Base). It aims to automatically answer natural language questions by retrieving structured data stored in the knowledge base. Generally, when a KBQA system receives the user’s query, it first needs to recognize topic entities of the query, such as name, location, organization, etc. This process is the NER (Named Entity Recognition). In this paper, we use the Bidirectional Long Short-Term Memory-Conditional Random Field (Bi-LSTM-CRF) model and introduce the SoftLexicon method for a Chinese NER task. At the same time, according to the analysis of the characteristics of application scenario, we propose a fuzzy matching module based on the combination of multiple methods. This module can efficiently modify the error recognition results, which can further improve the performance of entity recognition. We combine the NER model and the fuzzy matching module into an NER system. To explore the availability of the system in some specific fields, such as a power grid field, we utilize the power grid-related original data collected by the Hebei Electric Power Company to improve our system according to the characteristics of data in the power grid field. We innovatively make the dataset and high-frequency word lexicon in the power grid field, which makes our proposed NER system perform better in recognizing entities in the field of power grid. We used the cross-validation method for validation. The experimental results show that the F1-score of the improved NER model on the power grid dataset reaches 92.43%. After processing the recognition results by using the fuzzy matching module, about 99% of the entities in the test set can be correctly recognized. It proves that the proposed NER system can achieve excellent performance in the application scenario of a power grid. The results of this work will also fill the gap in the research of intelligent customer-service-related technologies in the power grid field in China.

In order to know whether a cooperative organization is really a cooperative in a true sense, it is necessary to first look at its identity. The identity of a cooperative is a basic characteristic inherent in a cooperative since its birth. It undergoes a very long and crystalline process of growth and maturity, thus placing the cooperative as a business entity that has a positioning for its members. Cooperatives operate in a space that is limited by their identity and by the rules that apply to free market players such as corporations. Cooperatives also play in a competitive market economy and also in an environment where market conditions sometimes do not apply. Therefore for cooperatives it is important to know where their position is at certain times in an effort to maintain and secure their identity, with the aim of maintaining their existence as a cooperative.

Background: Grid computing relates to a pool of resources to be shared by users in Grid Environment. Security of the resources from users and vice-versa is a significant issue. This is where the notion of trust comes into existence. A number of researchers have proposed models for evaluation of trust in grid computing, but they fail to spot one or the other parameters for trust evaluation. The essence of trust models in grid computing is that they offer autonomic trust management. An autonomic trust model has been patented by Z. Yan and C. Prehofer in 2009. Another patent was published by Anna University in 2010 to evaluate the trustworthiness of a resource provider in Grid environment. Objective: This paper firstly focuses and illustrates these essential parameters. Based on these parameters, further, a comparison of some existing models for trust evaluation is shown. Finally, common parameters missed out by various models have been highlighted giving way for improvements of Trust model. Methods: A Trust evaluation model has been proposed by us previously based on a number of real-world trust evaluation parameters. This model sees trust as a three-dimensional entity. Trust is based on Dempter Shafer’s theory in which trust is calculated mathematically. Results: Software trust needs to be calculated mathematically. There are a large number of real-world parameters that need to be included for evaluating trust. Conclusion: As trust models in research are based on simulation techniques, so it is important to include real-world factors that affect trust value of one entity on other. Some of those parameters, missed by most of the models have been identified for inclusion in future trust models.

Smart grids evolve rapidly towards a system that includes components from different domains, which makes interdisciplinary modelling and analysis indispensable. In this paper, we present a cosimulation architecture for smart grids together with a comprehensive data model for the holistic representation of the power system, the communication network, and the energy market. Cosimulation is preferred over a monolithic approach since it allows leveraging the capabilities of existing, well-established domain-specific software. The challenges that arise in a multidomain smart grid cosimulation are identified for typical use cases through a discussion of the recent literature. Based on the identified requirements and use cases, a joint representation of the smart grid ecosystem is facilitated by a comprehensive data model. The proposed data model is then integrated in a software architecture, where the domain-specific simulators for the power grid, the communication network, and the market mechanisms are combined in a cosimulation framework. The details of the software architecture and its implementation are presented. Finally, the implemented framework is used for the cosimulation of a virtual power plant, where battery storages are controlled by a novel peak-shaving algorithm, and the battery storages and the market entity are interfaced through a communication network.

For the simplicity of spatial modeling in Cellular Automaton (CA) and the complexity of vector spatial expression in the Multi-Agent System (MAS), the concept of grid object as the spatial model of individual behavior simulation was proposed with spatial information, semantic information, and connection relationship of geographic entity. Then, by incorporating the MAS, the method for individual behavior simulation with the Grid Object and Agent Model (GOAM) was demonstrated. Meanwhile, a prototype system including the three subsystems was developed based on the GOAM, and experiments were conducted for two cases in different spatial environments. The prototype system can be used to obtain grid object data with 3D model data, to compute and simulate the behavior of individuals, and to render individuals. The two cases involve goal-driven behavior in both indoor and outdoor environments as examples to evaluate the validity of the GOAM and to provide a reference for building individual behavior simulation with the GOAM in other scenarios.

Energy saving and emission reduction have become common concerns in countries around the world. In China, with the implementation of the new strategy of “carbon peak and neutrality” and the rapid development of the new smart grid infrastructure, the amount of data of actual power grid dispatching and fault analysis show exponential growth, which has led to phenomena such as poor supervision effectiveness and difficulty in handling faults in the process of grid operation and maintenance. Existing research on retrieval recommendation methods has had a lower accuracy rate at cold-start due to a small sample of user interactions. In addition, the cumulative learning of user personalization during general retrieval results in a poor perception of potential interest. By constructing a power knowledge graph, this paper presents a power fault retrieval and recommendation model (PF2RM) based on user-polymorphic perception. This model includes two methods: the power fault retrieval method (PFR) and the user-polymorphic retrieval recommendation method (UPRR). First, we take the power grid fault dispatching business as the core and reconstruct the ontology layer of the power knowledge graph. The PFR method is used to design the graph-neighbor fault entity cluster to enhance the polymerization degree of a fault implementation scenario. This method can solve the search cold-start recommendation problem. At the same time, the UPRR method aims to form user retrieval subgraphs of the past-state and current-state and make a feature matching for the graph-neighbor fault entity cluster, and then realize the accurate prediction of the user’s general search intention. The model is compared with other current classical models through the evaluation of multiple recommendation evaluation metrics, and the experimental results show that the model has a 3–8% improvement in the cold-start recommendation effect and 2–10% improvement in regular retrieval. The model has the best average recommendation performance in multiple metrics and has good results in fault analysis and retrieval recommendation. It plays a helpful role in intelligent operation and maintenance of the power grid and auxiliary decision-making, and effectively improves the reliability of the power grid.

So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied individually. Recently, a growing interest has been built for unified NER, tackling the above three jobs concurrently with one single model. Current best-performing methods mainly include span-based and sequence-to-sequence models, where unfortunately the former merely focus on boundary identification and the latter may suffer from exposure bias. In this work, we present a novel alternative by modeling the unified NER as word-word relation classification, namely W^2NER. The architecture resolves the kernel bottleneck of unified NER by effectively modeling the neighboring relations between entity words with Next-Neighboring-Word (NNW) and Tail-Head-Word-* (THW-*) relations. Based on the W^2NER scheme we develop a neural framework, in which the unified NER is modeled as a 2D grid of word pairs. We then propose multi-granularity 2D convolutions for better refining the grid representations. Finally, a co-predictor is used to sufficiently reason the word-word relations. We perform extensive experiments on 14 widely-used benchmark datasets for flat, overlapped, and discontinuous NER (8 English and 6 Chinese datasets), where our model beats all the current top-performing baselines, pushing the state-of-the-art performances of unified NER.

Abstract In view of the difficulty of data inventory caused by the large amount of accumulated data of the current grid business data, multiple data sources, and complex data, this paper proposes a data inventory scheme for structured data and unstructured data. Firstly, this paper uses the YOLOv3 deep learning network to label unstructured data to convert unstructured data into structured data; Secondly, the Semantic Hypergraph Clustering (SHC) method is used to realize the identification of the same entity of the power grid data and the elimination of redundancy. This method introduces semantic information in the block; Finally, in the comparative experiments of AR and RR, 96.88% and 95.03% respectively verify the effectiveness of the scheme.

Abstract DEM is an important data source to describe the surface morphology, but it is not a real 3D model, which can not meet the requirements of true 3D description under the ground. LIDAR point cloud data is a new true 3D data with high precision and high density. Based on the analysis of the differences between DEM and point cloud data in acquisition method, data structure and model construction, this paper proposes a 3D point set data model based on regular grid 2D data field, as well as the idea of regional modeling, and tests the feasibility of the data model through the upper and lower boundary modeling method. The experiments show that: (1) the 3D point set data model based on regular grid 2D data field is compatible with complete DEM data and simplified point cloud data, and has good expansibility; (2) the newly-built data model can complete the true 3D modeling of simple underground entity with high efficiency when the amount of data is only doubled; (3) The new data model can be generated by inputting DEM data, point cloud data and simplified algorithm of point cloud data under the same coordinate system. It has the potential of large-scale, multi-scale and automatic output processing, and has a good prospect of popularization.

A three-dimensional entity model of storage grid semi-trailer frame was designed using the Pro/E software. The model was transferred into the ANSYS for static strength analysis. On this basis, bionic structure was introduced into design of semi-trailer frame. The static strength of bionic and traditional structure was contrasted under the same conditions of weight.

Indoor space information extraction is an important aspect of reconstruction for building information modeling and a necessary process for geographic information system from outdoor to indoor. Entity model extracting methods provide advantages in terms of accuracy for building indoor spaces, as compared with network and grid model methods, and the extraction results can be converted into a network or grid model. However, existing entity model extracting methods based on a search loop do not consider the complex indoor environment of a building, such as isolated columns and walls or cross-floor spaces. In this study, such complex indoor environments are analyzed in detail, and a new approach for extracting buildings’ indoor space information is proposed. This approach is based on indoor space boundary calculation, the Boolean difference for single-floor space extraction, relationship reconstruction, and cross-floor space extraction. The experimental results showed that the proposed method can accurately extract indoor space information from the complex indoor environment of a building with geometric, semantic, and relationship information. This study is theoretically important for better understanding the complexity of indoor space extraction and practically important for improving the modeling accuracy of buildings.

Named entity identification is an important step in building a knowledge graph of the grid domain, which contains a certain number of nested entities. To address the issue of nested entities in the Chinese power dispatching domain’s named entity recognition, we propose a RoBERTa-Attention-FL model. This model effectively recognizes nested entities using the span representation annotation method. We extract the output values from RoBERTa’s middle 4–10 layers, obtain syntactic information from the Transformer Encoder layers via the multi-head self-attention mechanism, and integrate it with deep semantic information output from RoBERTa’s last layer. During training, we use Focal Loss to mitigate the sample imbalance problem. To evaluate the model’s performance, we construct named entity recognition datasets for flat and nested entities in the power dispatching domain annotated with actual power operation data, and conduct experiments. The results indicate that compared to the baseline model, the RoBERTa-Attention-FL model significantly improves recognition performance, increasing the F1-score by 4.28% to 90.35%, with an accuracy rate of 92.53% and a recall rate of 88.12%.

Over time, the number of smart grids installed worldwide is gradually increasing. However, the major portion of the required electricity is still being produced by traditional large-scale and centralized power systems. The main requirement, then, is to study and develop mathematical methods that attend the integration between the two systems previously announced. In this paper, a novel model that addresses this issue is presented. The model minimizes the total operating cost of the large-scale system considering the participation of the smart grid as a dynamic entity, entailing a close relationship between both systems. This approach distinguishes the novel proposal from others that solve similar situations by taking into account the two systems in isolation. Besides, the models that represent the most common organizational structures of the smart grids are also presented in this paper. They are needed to develop the integrated model. Many similar problems in the literature are solved by implementing decomposition techniques, which might obtain a local optimum different from the global one. By contrast, problems with this proposal are solved by using mixed-integer linear programming models that ensure the reaching of a global optimum. The real test case is the integrated Argentine large-scale system and the Armstrong smart grid. Results indicate that the novel model can reach solutions that are 5% lower in comparison with the traditional techniques of considering in isolation. Efficient CPU times enable the possibility of promptly obtaining solutions if there is any change in the parameters. In addition, other benefits, apart from the economical reductions, are also achieved. Operating information closer to the reality of both systems is obtained because it considers the effects of the smart grid in large-scale system solving.

A non-linear control progression for 3-stage (phase) lattice (grid) associated of PV generator is proposed here. This system is designed with PV arrays; grid filter; a voltage source inverter and a stimulating lattice or network. The regulator purposes are classified into three sections: i) making sure that the Most power point tracking (MPPT) are having photovoltaic boards, ii) assuring for power or control factor entity or unit in the grid plane iii) ensuring large-scale asymptotic constancy of the closed loop system. Lyapunov modelling approach is used by the controller and carried out by considering nonlinear model of the integral method. It is formally shown that the projected system controller congregates the scope of the objectives using a hypothetical constancy with stability analysis as well as simulation results.

Cooperatives as a business entity are expected to have a competitive advantage compared to other business entities, thereby providing optimal service to members. In running this business, cooperatives are companies and organizations, so in carrying out their activities they consistently carry out a cooperative identity. For this reason, this study was conducted to find out how the competitive advantages of cooperatives consistently carry out cooperative identity by using the ICA Grid instrument. This research is a case study and was held in Koperasi Pertenak Sapi Bandung Selatan (The Cooperative of Cattle Farmer in South of Bandung) that cooperatives must have a greater role in their business activities.

This article presents and discusses empirical research into citizen satisfaction, conducted to measure the performance and management of local public policies. The object of the research is to evaluate the public safety policy of a Spanish city through a survey measuring citizen satisfaction with the local public services. Regression and ‘importance-performance analysis’ (IPA) were applied to the views expressed by a sample of citizens. One objective of the study reported is to use different derived importance values, from correlation and regression analysis, to compare the results of two ‘importance-performance analyses’. The concept of variant and invariant weights and the three-factor theory of customer satisfaction were also considered for the research. Results confirm that the ‘fire service’ is seen as a ‘dissatisfier’, a basic or flat attribute; they suggest that negative coefficient values could be associated with ‘dissatisfiers’ and that inherent discontinuity is only partly resolved in the partition importance-performance grid. The findings show that the results of the two IPAs differ using the traditional IPA grid but are similar with diagonal models of the partition IPA grid. Points for practitioners One of the main problems for political decision-makers in recent years has been the reconfiguration of the budget for the entity managed, such as a municipality, in the current situation of economic crisis. This article contributes to improving decision-making on the reallocation of money and resources in the face of restrictions and financial cuts.

The current paper defines a framework for the introduction of frequency containment reserve (FCR) services, enabled by vehicle-to-grid (V2G) technology, into the business model of an entity owning and operating electric vehicle (EV) charging infrastructure. Moreover, the defined framework can also be extrapolated, with minor adjustments, to the business models of different core participants of the EV charging business ecosystem. This study also investigates the financial factors impacted by this introduction, eventually evaluating its financial profitability under given assumptions and comparing it to the profitability of the traditional business model of an entity owning and operating a unidirectional EV charging infrastructure. The current research shows that offering additional V2G-enabled FCR services can be potentially more profitable than the existing unidirectional approach if the V2G technology reaches its maturity phase with mass market adoption and economies of scale.

Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. However, existing coherence models focus on measuring individual aspects of coherence, such as lexical overlap, entity centralization, rhetorical structure, etc., lacking measurement of the semantics of text. In this paper, we propose a discourse coherence analysis method combining sentence embedding and the dimension grid, we obtain sentence-level vector representation by deep learning, and we introduce a coherence model that captures the fine-grained semantic transitions in text. Our work is based on the hypothesis that each dimension in the embedding vector is exactly assigned a stated certainty and specific semantic. We take every dimension as an equal grid and compute its transition probabilities. The document feature vector is also enriched to model the coherence. Finally, the experimental results demonstrate that our method achieves excellent performance on two coherence-related tasks.

This paper describes the influences of transmission line on the safety and reliability of grid, and illustrates the necessary of visual management of transmission lines. Virtual 3d scene modeling method is introduced in detail, including Virtual 3d scene modeling and virtual 3d entity modeling. According to the characteristics of transmission line, the 3d scene of transmission line ia designed and implemented. At last, Virtual reality modeling language (VRML) is used to complete the optimization of the established model and the experimental results show that the data size of the optimized model has reduced considerably.

Structural behavior of piezoelectric stacks has a great impact on electromechanical conversion efficiency of highway piezoelectric generators. In this paper, a single piezoelectric ceramic chip in piezoelectric stack structure was regarded as the object of study to construct the ANSYS model of a single piezoelectric ceramic entity. The selection of SOLID5 makes the piezoelectric ceramic chip into the grid cube and by the coupling field, maintains five aspects of static and dynamic piezoelectric coupling characteristic simulation analysis respectively, including static analysis, modal analysis, harmonic analysis, transient analysis and circuit simulation analysis. The results of all the analyses for the optimal structure design of highway piezoelectric generators are to provide a reference.

This paper is oriented to the credit investigation scenario of power grid supply chain enterprises and proposes a blockchain user credit assessment method based on improved Softmax regression in Power IoT. This method first designs a credit-rating mechanism that meets industry characteristics based on business needs. Second, it proposes a user credit evaluation model based on the blockchain architecture. Finally, the improved Softmax regression algorithm is used to train the proposed credit evaluation model, which effectively solves the credit rating. The multiclassification problem has achieved the goal of categorizing the credit rating of the enterprise. The simulation results show that the credit evaluation mechanism proposed in this paper can accurately evaluate the multisource credit data that lacks trust foundation and effectively realize the credit rating of power grid material supply chain enterprises. The credit evaluation mechanism proposed for Power IoT in this paper could have high potential for entity identity authentication and rating for securing mobile video communications.

stract: Security for authentication is required to give a superlative secure users’ personal information. This paper presents a model of the Graphical password scheme under the impact of security and ease of use for user authentication. We integrate the concept of recognition with re-called and cued- recall based schemes to offer superior security compared to existing schemes. Click Symbols (CS) Alphabet combine into one entity: Alphanumeric (A) and Visual (V) symbols (CS-AV) is Captcha-based password scheme, we integrate it with recall- based n×n grid points, where a user can draw the shape or pattern by the intersection of the grid points as a way to enter a graphical password. Next scheme, the combination of CS-AV with grid cells allows very large password space (2.4 × 104 bits of entropy) and provides reasonable usability results by determining an empirical study of memorable password space. Proposed schemes support most applicable platform for input devices and promising strong resistance to shoulder surfing attacks on a mobile device which can be occurred during unlocking (pattern) the smartphone

Based on the theory of two-phase flow, the flow field of dust particles inside the wet electrostatic precipitator with SIMPLE algorithm was modeled used a standard turbulence model and simulated numerically. Establishment of finite element model for the entity porous plate was done with grid refined locally. The number and installation position of porous plates, the diverging angle of the horn inlet and the opening rate of air distribution plates were changed in this simulation. And the studies on the airflow distribution of the inlet section were performed, in which the flow of key parameters for air flow uniform distribution are given. The experimental verification of optimal results of simulation model suggested that the simulation results and the experimental results were coincident.

Presently, the State Grid Corporation of China has accumulated a large amount of maintenance records for power primary equipment. Unfortunately, most of these records are unstructured data which lead to difficultly analyze and utilize them. The emergence of natural language processing technology and deep learning methods provide a solution for unstructured text data. This paper proposes a progressive multitype feature fusion model to recognize Chinese named entity of unstructured maintenance records for power primary equipment. Firstly, the textual characteristics and word separation difficulties of maintenance records are analyzed, then 7 main entity categories of power technical terms from unstructured maintenance records are chosen, and 3452 maintenance records are labeled by these categories, which is so called EPE-MR training dataset. Secondly, the standard test reports, standard maintenance, and fault analysis reports for three types of power primary equipment (namely, main transformer, circuit breaker, and isolating switch) are employed as corpus to train character embedding in order to obtain certain words representation ability of maintenance records. After that, progressive multilevel radicals feature extraction module is designed to get detailed and fine semantic information in a hierarchical manner. Further, radicals feature representation and character embedding are concatenated and sent to BiLSTM module to extract contextual information in order to improve Chinese entity recognition ability. Moreover, CRF is introduced to handle the dependencies among prediction labels and to output the optimal prediction sequence, which can easily obtain structured data of maintenance records. Finally, comparative experiments on public MSRA dataset, China People’s Daily corpus, and EPE-MR dataset are implemented, respectively, which show the effectiveness of the proposed method.

This paper examines the theory and practice of electricity federalism in the Nigerian federation. Although Nigeria is an American-styled federal entity, its practice does not reflect the true principles of federalism as practiced in America. Nigeria's electricity sector is a reflection of its imperfect practice of federalism. The effect is felt in the poor performance of the electricity sector, especially off-grid undertakings. Thus, this study turns to the practice of electricity federalism in the United State of America as a model federalist system from which Nigeria can draw inspiration towards a better practice of electricity federalism. Evidence from America demonstrates how fiscal federalism led to a robustly developed power sector. It is argued that, although constituent states of Nigeria have the legal capacity, they lack the wherewithal to develop robust off-grid electricity undertaking under the current federalist system. Hence, political restructuring that would ensure fiscal federalism is needful in Nigeria.

In the pilot provinces of China’s electricity spot market, power generation companies usually adopt the separate bidding mode, which leads to a low willingness of demand-side response and poor flexibility in the interaction mechanism between supply and demand. Based on the analysis of the demand response mechanism of the power day-ahead market with the participation of power sales companies, this paper abstracted the game process of the “power grid-sales company-users” tripartite competition in the electricity market environment into a two-layer (purchase layer/sales layer) game model and proposed a master–slave game equilibrium optimization strategy for the day-ahead power market under the two-layer game. The multi-objective multi-universe optimization algorithm was used to find the Pareto optimal solution of the game model, a comprehensive evaluation was constructed, and the optimal strategy of the demand response was determined considering the peak cutting and valley filling quantity of the power grid, the profit of the electricity retailers, the cost of the consumers, and the comfort degree. Examples are given to simulate the day-ahead electricity market participated in by the electricity retailers, analyze and compare the benefits of each market entity participating in the demand response, and verify the effectiveness of the proposed model.

As an independent building entity on the sea, the ship has a large number of internal electrical equipment and a compact space structure, which is prone to fire. This paper proposes a key technology of virtual dynamic escape of ships based on the fire spread prediction model for research. Taking the 63,500 DWT(Dead Weight Tonnage) tanker cabin as a research entity, the mathematical and physical models of ship fire simulation are established. Through the graphical analysis of the experimental data of the fire spread simulation, the temperature, CO concentration, and smoke concentration change rules under different working conditions at the fixed detection point position are obtained. Then, based on temperature, CO concentration and smoke concentration three impact factors, set up a comprehensive fire real-time situational risk evaluation index system. Using the MATLAB software, based on the principle of the fuzzy neural network fire ship’s integrated real-time situational risk evaluation model structure design and simulation test, obtained the corresponding training to comprehensive risk evaluation model of the network. Generate navigation grid according to the law of fire sprawl, and plan escape path. The traditional A* algorithm is improved, and an example is used to prove that the path-finding result after the improved algorithm is shorter than the path found by the traditional algorithm, which meets the path-finding requirements in a three-dimensional environment.

Nowadays, voluminous and unstructured textual data is found on the Internet that could provide varied valuable information for different institutions such as health care, business-related, training, religion, culture, and history, among others. A such alarming growth of unstructured data fosters the need for various methods and techniques to extract valuable information from unstructured data. However, exploring helpful information to satisfy the needs of the stakeholders becomes a problem due to information overload via the internet. This paper, therefore, presents an effective model for extracting named entities from Ge'ez text using deep learning algorithms. A data set with a total of 5,270 sentences were used for training and testing purposes. Two experimental setups, i.e., long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM) were used to make an empirical evaluation with training and a testing split ratio of 80% to 20%, respectively. Experimental results showed that the proposed model could be a practical solution for building information extraction (IE) systems using Bi-LSTM, reaching a training, validation, and testing accuracy as high as 98.59%, 97.96%, and 96.21%, respectively. 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Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 5"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 6"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 7"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 8"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" Name="toc 9"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Normal Indent"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="footnote text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="annotation text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="header"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="footer"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="index heading"/> <w:LsdException Locked="false" Priority="35" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="caption"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="table of figures"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="envelope address"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="envelope return"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="footnote reference"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="annotation reference"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="line number"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="page number"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="endnote reference"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="endnote 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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Bullet 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Bullet 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Bullet 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Number 5"/> <w:LsdException Locked="false" Priority="10" QFormat="true" Name="Title"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Closing"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Signature"/> <w:LsdException Locked="false" Priority="1" SemiHidden="true" UnhideWhenUsed="true" Name="Default Paragraph Font"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text Indent"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="List Continue 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Message Header"/> <w:LsdException Locked="false" Priority="11" QFormat="true" Name="Subtitle"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Salutation"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Date"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text First Indent"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text First Indent 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Note Heading"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text Indent 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Body Text Indent 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Block Text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Hyperlink"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="FollowedHyperlink"/> <w:LsdException Locked="false" Priority="22" QFormat="true" Name="Strong"/> <w:LsdException Locked="false" Priority="20" QFormat="true" Name="Emphasis"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Document Map"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Plain Text"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="E-mail Signature"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Top of Form"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Bottom of Form"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Normal (Web)"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Acronym"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Address"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Cite"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Code"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Definition"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Keyboard"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Preformatted"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Sample"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Typewriter"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="HTML Variable"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Normal Table"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="annotation subject"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="No List"/> <w:LsdException 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UnhideWhenUsed="true" Name="Table Colorful 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Colorful 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Colorful 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Columns 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 6"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 7"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Grid 8"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 4"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 5"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 6"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 7"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table List 8"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table 3D effects 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table 3D effects 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table 3D effects 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Contemporary"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Elegant"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Professional"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Subtle 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Subtle 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Web 1"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Web 2"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Web 3"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Balloon Text"/> <w:LsdException Locked="false" Priority="39" Name="Table Grid"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Table Theme"/> <w:LsdException Locked="false" SemiHidden="true" Name="Placeholder Text"/> <w:LsdException Locked="false" Priority="1" QFormat="true" Name="No Spacing"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading"/> <w:LsdException Locked="false" Priority="61" Name="Light List"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3"/> <w:LsdException Locked="false" Priority="70" Name="Dark List"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 1"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 1"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 1"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 1"/> <w:LsdException Locked="false" Priority="64" Name="Medium 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Name="Colorful Grid Accent 1"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 2"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 2"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 2"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 2"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 2"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 2"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 2"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 2"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 2"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 2"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 2"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 2"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 2"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 2"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 3"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 3"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 3"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 3"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 3"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 3"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 3"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 3"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 3"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 3"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 3"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 3"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 3"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 3"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 4"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 4"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 4"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 4"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 4"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 4"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 4"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 4"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 4"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 4"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 4"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 4"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 4"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 4"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 5"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 5"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 5"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 5"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 5"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 5"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 5"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 5"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 5"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 5"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 5"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 5"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 5"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 5"/> <w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 6"/> <w:LsdException Locked="false" Priority="61" Name="Light List Accent 6"/> <w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 6"/> <w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 6"/> <w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 6"/> <w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 6"/> <w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 6"/> <w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 6"/> <w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 6"/> <w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 6"/> <w:LsdException Locked="false" Priority="70" Name="Dark List Accent 6"/> <w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 6"/> <w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 6"/> <w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 6"/> <w:LsdException Locked="false" Priority="19" QFormat="true" Name="Subtle Emphasis"/> <w:LsdException Locked="false" Priority="21" QFormat="true" Name="Intense Emphasis"/> <w:LsdException Locked="false" Priority="31" QFormat="true" Name="Subtle Reference"/> <w:LsdException Locked="false" Priority="32" QFormat="true" Name="Intense Reference"/> <w:LsdException Locked="false" Priority="33" QFormat="true" Name="Book Title"/> <w:LsdException Locked="false" Priority="37" SemiHidden="true" UnhideWhenUsed="true" Name="Bibliography"/> <w:LsdException Locked="false" Priority="39" SemiHidden="true" UnhideWhenUsed="true" QFormat="true" Name="TOC Heading"/> <w:LsdException Locked="false" Priority="41" Name="Plain Table 1"/> <w:LsdException Locked="false" Priority="42" Name="Plain Table 2"/> <w:LsdException Locked="false" Priority="43" Name="Plain Table 3"/> <w:LsdException Locked="false" Priority="44" Name="Plain Table 4"/> <w:LsdException Locked="false" Priority="45" Name="Plain Table 5"/> <w:LsdException Locked="false" Priority="40" Name="Grid Table Light"/> <w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light"/> <w:LsdException Locked="false" Priority="47" Name="Grid Table 2"/> <w:LsdException Locked="false" Priority="48" Name="Grid Table 3"/> <w:LsdException Locked="false" Priority="49" Name="Grid Table 4"/> <w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark"/> <w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful"/> <w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful"/> 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<w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 6"/> <w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful Accent 6"/> <w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful Accent 6"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Mention"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Smart Hyperlink"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Hashtag"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Unresolved Mention"/> <w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true" Name="Smart Link"/> </w:LatentStyles> </xml><![endif]--><!--[if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman",serif; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} </style> <![endif]--><p class="MsoNormal" style="text-align: justify;"><span lang="EN-US">Bi-LSTM;<strong style="mso-bidi-font-weight: normal;"><em style="mso-bidi-font-style: normal;"></em></strong></span></p><p class="MsoNormal" style="text-align: justify;"><span lang="EN-US">Deep learning;</span></p><p class="MsoNormal" style="text-align: justify;"><span lang="EN-US">Entity extraction;</span></p><p class="MsoNormal" style="text-align: justify;"><span lang="EN-US">Ge’ez text;</span></p><span style="font-size: 10.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">Information extraction</span>

Renewable energy systems (RES) are no longer confined to being used as a stand-alone entity in the modern era. These RES, especially solar panels are also used with the grid power systems to supply electricity. However, precise forecasting of solar irradiance is necessary to ensure that the grid operates in a balanced and planned manner. Various solar forecasting models (SFM) are presented in the literature to produce an accurate solar forecast. Nevertheless, each model has gone through the step of evaluation of its accuracy using some error measures. Many error measures are discussed in the literature for deterministic as well as probabilistic solar forecasting. But, each study has its own selected error measure which sometimes landed on a wrong interpretation of results if not selected appropriately. As a result, this paper offers a critical assessment of several common error metrics with the goal of discussing alternative error metrics and establishing a viable set of error metrics for deterministic and probabilistic solar forecasting. Based on highly cited research from the last three years (2019-2021), error measures for both types of forecasting are presented with their basic functionalities, advantages & limitations which equipped the reader to pick the required compatible metrics

The concept of the digital twin has been adopted as an important aspect in digital transformation of power systems. Although the notion of the digital twin is not new, its adoption into the energy sector has been recent and has targeted increased operational efficiency. This paper is focused on addressing an important gap in the research literature reviewing the state of the art in utilization of digital twin technology in microgrids, an important component of power systems. A microgrid is a local power network that acts as a dependable island within bigger regional and national electricity networks, providing power without interruption even when the main grid is down. Microgrids are essential components of smart cities that are both resilient and sustainable, providing smart cities the opportunity to develop sustainable energy delivery systems. Due to the complexity of design, development and maintenance of a microgrid, an efficient simulation model with ability to handle the complexity and spatio-temporal nature is important. The digital twin technologies have the potential to address the above-mentioned requirements, providing an exact virtual model of the physical entity of the power system. The paper reviews the application of digital twins in a microgrid at electrical points where the microgrid connects or disconnects from the main distribution grid, that is, points of common coupling. Furthermore, potential applications of the digital twin in microgrids for better control, security and resilient operation and challenges faced are also discussed.

Abstract Purpose Due to the incompleteness nature of knowledge graphs (KGs), the task of predicting missing links between entities becomes important. Many previous approaches are static, this posed a notable problem that all meanings of a polysemous entity share one embedding vector. This study aims to propose a polysemous embedding approach, named KG embedding under relational contexts (ContE for short), for missing link prediction. Design/methodology/approach ContE models and infers different relationship patterns by considering the context of the relationship, which is implicit in the local neighborhood of the relationship. The forward and backward impacts of the relationship in ContE are mapped to two different embedding vectors, which represent the contextual information of the relationship. Then, according to the position of the entity, the entity's polysemous representation is obtained by adding its static embedding vector to the corresponding context vector of the relationship. Findings ContE is a fully expressive, that is, given any ground truth over the triples, there are embedding assignments to entities and relations that can precisely separate the true triples from false ones. ContE is capable of modeling four connectivity patterns such as symmetry, antisymmetry, inversion and composition. Research limitations ContE needs to do a grid search to find best parameters to get best performance in practice, which is a time-consuming task. Sometimes, it requires longer entity vectors to get better performance than some other models. Practical implications ContE is a bilinear model, which is a quite simple model that could be applied to large-scale KGs. By considering contexts of relations, ContE can distinguish the exact meaning of an entity in different triples so that when performing compositional reasoning, it is capable to infer the connectivity patterns of relations and achieves good performance on link prediction tasks. Originality/value ContE considers the contexts of entities in terms of their positions in triples and the relationships they link to. It decomposes a relation vector into two vectors, namely, forward impact vector and backward impact vector in order to capture the relational contexts. ContE has the same low computational complexity as TransE. Therefore, it provides a new approach for contextualized knowledge graph embedding.

Abstract Purpose Due to the incompleteness nature of knowledge graphs (KGs), the task of predicting missing links between entities becomes important. Many previous approaches are static, this posed a notable problem that all meanings of a polysemous entity share one embedding vector. This study aims to propose a polysemous embedding approach, named KG embedding under relational contexts (ContE for short), for missing link prediction. Design/methodology/approach ContE models and infers different relationship patterns by considering the context of the relationship, which is implicit in the local neighborhood of the relationship. The forward and backward impacts of the relationship in ContE are mapped to two different embedding vectors, which represent the contextual information of the relationship. Then, according to the position of the entity, the entity's polysemous representation is obtained by adding its static embedding vector to the corresponding context vector of the relationship. Findings ContE is a fully expressive, that is, given any ground truth over the triples, there are embedding assignments to entities and relations that can precisely separate the true triples from false ones. ContE is capable of modeling four connectivity patterns such as symmetry, antisymmetry, inversion and composition. Research limitations ContE needs to do a grid search to find best parameters to get best performance in practice, which is a time-consuming task. Sometimes, it requires longer entity vectors to get better performance than some other models. Practical implications ContE is a bilinear model, which is a quite simple model that could be applied to large-scale KGs. By considering contexts of relations, ContE can distinguish the exact meaning of an entity in different triples so that when performing compositional reasoning, it is capable to infer the connectivity patterns of relations and achieves good performance on link prediction tasks. Originality/value ContE considers the contexts of entities in terms of their positions in triples and the relationships they link to. It decomposes a relation vector into two vectors, namely, forward impact vector and backward impact vector in order to capture the relational contexts. ContE has the same low computational complexity as TransE. Therefore, it provides a new approach for contextualized knowledge graph embedding.

Abstract- Cloud computing is Internet-based computing, whereby shared resources, software and information, are provided to computers and devices on-demand, like the electricity grid. It aims to construct a perfect system with powerful computing capability through a large number of relatively low-cost computing entity, and using the advanced business models like SaaS (Software as a Service), PaaS (Platform as a Service), IaaS (Infrastructure as a Service) to distribute the powerful computing capacity to end users’ hands. Cloud Computing represents a new computing model that poses many demanding security issues at all levels, e.g., network, host, application, and data levels. The variety of the delivery models presents different security challenges depending on the model and consumers’ Quality of Service (QoS) requirements. Confidentiality, Integrity, Availability, Authenticity, and Privacy are essential concerns for both Cloud providers and consumers as well. This paper introduces the existing issues in cloud computing such as security, privacy, reliability and so on. This paper surveys the security problems of current cloud computing.

With the gradual liberalization of China’s energy market, the distributed characteristics of each entity in the community integrated energy system are more and more obvious, and the traditional centralized optimization is difficult to reveal the interaction between the entities. This paper aims to improve the profit of the community operator and the users’ value-added benefit of energy use, and proposes a multi-energy transaction decision of a community integrated energy system considering user interaction. First, a refined model of user interaction, including energy conversion, is established, and then the optimization model of multi-energy transaction decision between the community operator and the users is constructed based on the master–slave game. The upper layer aims to maximize the profit of the community operator according to the energy use strategies’ feedback from the users, decides the retail energy prices of the community operator to the users, and optimization variables include equipment output and energy purchased from the power grid and natural gas grid. The lower layer aims to maximize the value-added benefit of energy use for users. The users optimize their energy use strategies based on the retail energy prices published by the community operator. The model is solved by the differential evolution algorithm combined with the CPLEX solver. Finally, different scenarios are analyzed in a numerical example, and the results show that the strategy proposed in this paper to set community prices increases the community operator’s profit and profit margin by 5.9% and 7.5%, respectively, compared to using market energy prices directly. At the same time, the value-added benefit to users also increases by 15.2%. The community operator and users can achieve a win–win situation.

Next-generation power systems will require innovative control strategies to exploit existing and potential capabilities of developing renewable-based microgrids. Cooperation of interconnected microgrids has been introduced recently as a promising solution to improve the operational and economic performance of distribution networks. In this paper, a hierarchical control structure is proposed for the integrated operation management of a multi-microgrid system. A central energy management entity at the highest control level is responsible for designing a reference trajectory for exchanging power between the multi-microgrid system and the main grid. At the second level, the local energy management system of individual microgrids adopts a two-stage stochastic model predictive control strategy to manage the local operation by following the scheduled power trajectories. An optimal solution strategy is then applied to the local controllers as operating set-points to be implemented in the system. To distribute the penalty costs resulted from any real-time power deviation systematically and fairly, a novel methodology based on the line flow sensitivity factors is proposed. Simulation and experimental analyses are carried out to evaluate the effectiveness of the proposed approach. According to the simulation results, by adopting the proposed operation management strategy, a reduction of about 47% in the average unplanned daily power exchange of the multi-microgrid system with the main grid can be achieved.

A multiperiod generation and transmission expansion planning (G&TEP) problem is considered. This model integrates conventional generation with renewable energy sources, assuming a stochastic approach. The proposed approach is based on a centralized planned transmission expansion. Due to the worldwide recent energy guidelines, it is necessary to generate expansion plans adequate to the forecast demand over the next years. Nowadays, in most energy systems, a public entity develops both the short and long of electricity-grid expansion planning. Due to the complexity of the problem, there are different strategies to find expansion plans that satisfy the uncertainty conditions addressed. We proposed to address the G&TEP problem with a pure genetic algorithm approach. Different constraint-handling techniques were applied to deal with two complex case studies presented. Numerical results are shown to compare the strategies used in the test systems, and key factors such as a prior initialization of population and the estimated minimum number of generations are discussed.

To alleviate the shortcomings of large-scale grid connections for clean energy, which require stable thermoelectric units to provide backup services, a stable cooperative alliance among different energy types of power sellers must be established. Consequently, a reasonable method to distribute income is required, due to different contributions of each entity in the alliance. Therefore, this paper constructs a comprehensive correction algorithm for income distribution using an improved Shapely value method. We analyze the operating mode of the power seller, and establish the net income calculation model under both independent and alliance operations. We then establish an alliance operation optimization model that considers the constraints of unit output, as well as the balance between supply and demand, with the goal of maximizing income. Finally, an industrial park in a province of northern China is taken as an example to verify the model’s practicability and effectiveness. The results show that the power sales alliance can effectively promote clean energy consumption. The maximum reduction in thermal power generation and CO2 is 8510 MW and 684.515 tons, respectively. We apply the algorithm to income distribution and find that the thermal power seller’s income increased by ¥1,463,870, which enhances the stability of the alliance. Therefore, our income distributing optimization model guarantees the interests of each participant to the greatest extent, and serves as an important reference for income distribution.

The power system is moving away from the centralized generation paradigm. One of the current trends is the microgrid concept, where loads, small generators and renewable energy resources (RERs) that are in close proximity are controlled as one entity. Microgrids also allow for an increase in power availability as they can continue to supply electric power to loads even in the absence of a connection to the main grid. During the transition to islanded operation, microgrids may be subject to frequency disturbances caused by the power imbalance between load and generation. When microgrids contain high shares of renewable energy, the challenge is significantly higher due to the control strategies that aim to maximize power production, which are typically applied to RERs and render them insensitive to grid changes. Therefore, new control strategies need to be developed to enable the participation of RERs in the support of the frequency response. This work proposes a predictive control strategy that is based on a generalized predictive controller (GPC) being applied to the grid side converter of a doubly fed induction generator (DFIG) wind turbine to enable frequency support capabilities. The control objective was to track a time varying power reference signal that was generated according to the deviation from the nominal frequency, thereby enabling the energy storage device to inject power into the microgrid without a communication system. The GPC is a controller belonging to the family of model predictive controllers (MPCs), the main principles of which are the use of a system model to predict future states and the choice of an optimal input to ensure that the reference values are followed. To validate the proposed control strategy, a microgrid was simulated in MATLAB Simscape Electrical. The frequency response using the proposed GPC strategy was compared to another MPC-based strategy, known as finite control set, and a scenario in which the DFIG was not equipped with frequency support capabilities. The results show that the proposed strategy was able to improve the frequency response of the microgrid, reduce frequency oscillations and increase the value of the frequency nadir.

Demand response as a distributed resource has proved its significant potential for power systems. It is capable of providing flexibility that, in some cases, can be an advantage to suppress the unpredictability of distributed generation. The ability for participating in demand response programs for small or medium facilities has been limited; with the new policy regulations this limitation might be overstated. The prosumers are a new entity that is considered both as producers and consumers of electricity, which can provide excess production to the grid. Moreover, the decision-making in facilities with different generation resources, energy storage systems, and demand flexibility becomes more complex according to the number of considered variables. This paper proposes a demand response optimization methodology for application in a generic residential house. In this model, the users are able to perform actions of demand response in their facilities without any contracts with demand response service providers. The model considers the facilities that have the required devices to carry out the demand response actions. The photovoltaic generation, the available storage capacity, and the flexibility of the loads are used as the resources to find the optimal scheduling of minimal operating costs. The presented results are obtained using a particle swarm optimization and compared with a deterministic resolution in order to prove the performance of the model. The results show that the use of demand response can reduce the operational daily cost.

An updated compilation of oxygen-isotope ratio data for 562 sites in Antarctica shows a significant increase in the number of sites and an improvement in the representation of the coastal zone oyer previous versions. The data hase consists of ratio values (δ18O; multi-year mean 18O/16O relative to Standard Mean Ocean Water, in ‰) compiled as the dependent variable, together with data for the so-called independent variables: latitude, surface elevation, mean annual surface temperature and mean annual shortest distance to open ocean denoted by the 20% sea-ice concentration boundary. The problem of covariation between so-called independent variables is minimized using stepwise regression analyses. A general model is described using all the field data, and the regional variation at drainage-system scale is assessed by contrasting models for two physiographically distinct regions. in addition, entity-specific models are determined using data subsets for the conterminous grounded-ice and ice-shelf areas. Inversions of the specific models are applied to a 100 km grid data base to produce two contoured distributions of the ratio, one based on field data, and the other on remotely sensed data. The difference between these produces residuals that, relative to the summation of standard errors of the models, are small in most of the interior area of the ice sheet, and large in several areas of mountain and coastal regions, where interpolation and extrapolation of field data are particularly unreliable. Remotely sensed data generally produce ratio values which are isotopically cooler.

Abstract. Analysis of Earth system dynamics in the Anthropocene requires explicitly taking into account the increasing magnitude of processes operating in human societies, their cultures, economies and technosphere and their growing feedback entanglement with those in the physical, chemical and biological systems of the planet. However, current state-of-the-art Earth system models do not represent dynamic human societies and their feedback interactions with the biogeophysical Earth system and macroeconomic integrated assessment models typically do so only with limited scope. This paper (i) proposes design principles for constructing world–Earth models (WEMs) for Earth system analysis of the Anthropocene, i.e., models of social (world)–ecological (Earth) coevolution on up to planetary scales, and (ii) presents the copan:CORE open simulation modeling framework for developing, composing and analyzing such WEMs based on the proposed principles. The framework provides a modular structure to flexibly construct and study WEMs. These can contain biophysical (e.g., carbon cycle dynamics), socio-metabolic or economic (e.g., economic growth or energy system changes), and sociocultural processes (e.g., voting on climate policies or changing social norms) and their feedback interactions, and they are based on elementary entity types, e.g., grid cells and social systems. Thereby, copan:CORE enables the epistemic flexibility needed for contributions towards Earth system analysis of the Anthropocene given the large diversity of competing theories and methodologies used for describing socio-metabolic or economic and sociocultural processes in the Earth system by various fields and schools of thought. To illustrate the capabilities of the framework, we present an exemplary and highly stylized WEM implemented in copan:CORE that illustrates how endogenizing sociocultural processes and feedbacks such as voting on climate policies based on socially learned environmental awareness could fundamentally change macroscopic model outcomes.

Abstract. Models of planetary, atmospheric and oceanic circulation involve eddy viscosity and eddy diffusivity, KM and KH, that account for unresolved turbulent mixing and diffusion. The most sophisticated turbulent closure models used today for geophysical applications belong in the family of the Reynolds stress models. These models are formulated for the physical space variables; they consider a hierarchy of turbulent correlations and employ a rational way of its truncation. In the process, unknown correlations are related to the known ones via "closure assumptions'' that are based upon physical plausibility, preservation of tensorial properties, and the principle of the invariant modeling according to which the constants in the closure relationships are universal. Although a great deal of progress has been achieved with Reynolds stress closure models over the years, there are still situations in which these models fail. The most difficult flows for the Reynolds stress modeling are those with anisotropy and waves because these processes are scale-dependent and cannot be included in the closure assumptions that pertain to ensemble-averaged quantities. Here, we develop an alternative approach of deriving expressions for KM and KH using the spectral space representation and employing a self-consistent, quasi-normal scale elimination (QNSE) algorithm. More specifically, the QNSE procedure is based upon the quasi-Gaussian mapping of the velocity and temperature fields using the Langevin equations. Turbulence and waves are treated as one entity and the effect of the internal waves is easily identifiable. This model implies partial averaging and, thus, is scale-dependent; it allows one to easily introduce into consideration such parameters as the grid resolution, the degree of the anisotropy, and spectral characteristics, among others. Applied to turbulent flows affected by anisotropy and waves, the method traces turbulence anisotropization and shows how the dispersion relationships for linear waves are modified by turbulence. In addition, one can derive the internal wave frequency shift and the threshold criterion of internal wave generation in the presence of turbulence. The spectral method enables one to derive analytically various one-dimensional and three-dimensional spectra that reflect the effects of waves and anisotropy. When averaging is extended to all scales, the method yields a Reynolds-averaged, Navier-Stokes equations based model (RANS). This RANS model shows that there exists a range of Ri, approximately between 0.1 and 1, in which turbulence undergoes remarkable anisotropization; the vertical mixing becomes suppressed while the horizontal mixing is enhanced. Although KH decreases at large Ri and tends to its molecular value, KM remains finite and larger than its molecular value. This behavior is attributable to the effect of internal waves that mix the momentum but do not mix a scalar. In the Reynolds stress models, this feature is not replicated; instead, all Reynolds stress models predict KM→0 at some value of Ri≤1 which varies from one model to another. The presented spectral model indicates that there is no a single-valued critical Richardson number Ri at which turbulence is fully suppressed by stable stratification. This result is in agreement with large volume of atmospheric, oceanic and laboratory data. The new spectral model has been implemented in the K-ε format and tested in simulations of the stably stratified atmospheric boundary layers. The results of these simulations are in good agreement with the data collected in BASE, SHEBA and CASES99 campaigns. Implementation of the QNSE-derived KM and KH in the high-resolution weather prediction system HIRLAM results in significant improvement of its predictive skills.

Abstract BACKGROUND Recently, non-invasive characterization of brain tumors on MRI has emerged as a promising field of research. The identification of quantitative imaging biomarkers, also known as radiomics, may complement molecular characterization and thereby improve clinical management of neuro-oncologic patients. In this study we aimed to identify imaging predictors with improved performance over clinical parameters in order to stratify patients with brain metastases into high and low risk groups for overall survival (OS). MATERIAL AND METHODS 422 patients (allocated in a 3:1 ratio to a discovery [n=317] and test [n= 105] set) with first diagnosis of brain metastases from different primary tumors from two neuro-oncologic centers were included. In each patient, eight clinical features (age, gender, KPS, systemic disease status, presence of extracranial metastases, number of cerebral metastases, primary tumor and available individual prognostic molecular status eg HER2, BRAF,⋯) were gathered and a total of 321 radiomic MRI features (including shape, first-order and higher-order features) from cerebral MRI (contrast T1-weighted and apparent diffusion coefficient maps) were extracted. Radiomic and clinical features of patients in the discovery set were subjected to different machine learning models in order to classify patients into low- and high-risk groups for OS. By performing a comprehensive grid search the best machine learning model according to the macro-evaluated score and accuracy was identified and evaluated on the testing set. In addition, a subgroup analysis, based on the primary tumor entity was done and confusion matrices were calculated in order to evaluate final predictions. RESULTS With an extra trees classifier including all clinical and 30 radiomic features, we were able to stratify patients into a high- and low-risk group for OS (test set: macro-evaluated = 0.60, accuracy = 0.67). The best performing model was a gradient boosting model only including clinical features (test set: macro-evaluated = 0.62, accuracy = 0.72), while the radiomic features alone led to the poorest results (test set: macro-evaluated = 0.52, accuracy = 0.63). Interestingly, in the subgroup of melanoma patients, the predictive power of radiomic features outperformed the clinical and the combined (radiomics and clinical) features. With a prediction solely based on radiomic features, 80% and 67% of patients were correctly classified into the low- and the high-risk group for OS, respectively. CONCLUSION In conclusion, we found that in the entire study population of patients with brain metastases radiomic features did not allow for a better prediction of the clinical outcome compared to the clinical parameters alone. However, in the subgroup of melanoma patients the predictive power of radiomic features alone was superior compared to clinical features or all features combined.

Contents. Coding and processing large information content actualizes the problem of formalization of interdependence between information parameters of vector data coding systems on a single mathematical platform. Objective. The formalization of relationships between information parameters of vector data coding systems in the optimized basis of toroidal coordinate systems with the achievement of a favorable compromise between contradictory goals. Method. The method involves the establishing harmonious mutual penetration of symmetry and asymmetry as the remarkable property of real space, which allows use decoded information for forming the mathematical principle relating to the optimal placement of structural elements in spatially or temporally distributed systems, using novel designs based on the concept of Ideal Ring Bundles (IRB)s. IRBs are cyclic sequences of positive integers which dividing a symmetric sphere about center of the symmetry. The sums of connected sub-sequences of an IRB enumerate the set of partitions of a sphere exactly R times. Two-and multidimensional IRBs, namely the “Glory to Ukraine Stars”, are sets of t-dimensional vectors, each of them as well as all modular sums of them enumerate the set node points grid of toroid coordinate system with the corresponding sizes and dimensionality exactly R times. Moreover, we require each indexed vector data “category-attribute” mutually uniquely corresponds to the point with the eponymous set of the coordinate system. Besides, a combination of binary code with vector weight discharges of the database is allowed, and the set of all values of indexed vector data sets are the same that a set of numerical values. The underlying mathematical principle relates to the optimal placement of structural elements in spatially and/or temporally distributed systems, using novel designs based on tdimensional “star” combinatorial configurations, including the appropriate algebraic theory of cyclic groups, number theory, modular arithmetic, and IRB geometric transformations. Results. The relationship of vector code information parameters (capacity, code size, dimensionality, number of encodingvectors) with geometric parameters of the coordinate system (dimension, dimensionality, and grid sizes), and vector data characteristic (number of attributes and number of categories, entity-attribute-value size list) have been formalized. The formula system is derived as a functional dependency between the above parameters, which allows achieving a favorable compromise between the contradictory goals (for example, the performance and reliability of the coding method). Theorem with corresponding corollaries about the maximum vector code size of conversion methods for t-dimensional indexed data sets “category-attribute” proved. Theoretically, the existence of an infinitely large number of minimized basis, which give rise to numerous varieties of multidimensional “star” coordinate systems, which can find practical application in modern and future multidimensional information technologies, substantiated. Conclusions. The formalization provides, essentially, a new conceptual model of information systems for optimal coding and processing of big vector data, using novel design based on the remarkable properties and structural perfection of the “Glory to Ukraine Stars” combinatorial configurations. Moreover, the optimization has been embedded in the underlying combinatorial models. The favorable qualities of the combinatorial structures can be applied to vector data coded design of multidimensional signals, signal compression and reconstruction for communications and radar, and other areas to which the GUS-model can be useful. There are many opportunities to apply them to numerous branches of sciences and advanced systems engineering, including information technologies under the toroidal coordinate systems. A perfection, harmony and beauty exists not only in the abstract models but in the real world also.

The use of relatively small-scale distributed electric power generation sources is one of the key focus areas in the development of global industry and regional power generation. By integrating distributed generation sources into their on-site energy infrastructure, industrial consumers gain new characteristics and possibilities as entities of the power system that do not only consume power, but in fact can flexibly generate and deliver electricity to local and even centralized grids. This type of entity is called a distributed energy resource system with demand response (Russian: ‘active energy complex’). The purpose of this study is to lay the methodological foundation for the use of distributed energy resource systems with demand response in industrial sites under existing gas and power market conditions and for ensuring the synchronization of parameters that is necessary for managing complex energy consumption. This article provides an empirical study of the principles of the natural gas pricing under the demand volatility of regional markets and the Russian Mercantile Exchange. The article outlines the key drivers, as identified by the authors, that impact gas consumption by a distributed energy resource system, including demand characteristics, limitations and capacity of the gas network and the mode of gas consumption by an industrial enterprise and its generator. Accounting for all of these factors is essential for effective management and proper operational adjustment of a distributed energy resource system with demand response. The result of the study is a proprietary model and a tool for the management of distributed energy resource systems in integration with the gas demand management, which analyze the internal and external parameters of the industrial entity’s operations and its distributed energy resource system, as well as factors existing in the integrated distributed energy system where the consumer is able to buy natural gas in various market segments. The proprietary tool of distributed energy resource system management is based on the centralized control system, which combines performance analytics, operational scheduling of production and the distributed energy resource system, price planning for the wholesale and retail power markets, regional gas markets and exchange, monitoring all elements of the system, and assessment of different active energy management scenarios under various external and internal conditions impacting production and energy demand. Our proprietary tool has been successfully tested in a typical industrial site and was reported to deliver a significant electricity and gas cost-saving effect, which amounted to an 18 percent reduction in the total energy costs of the company, or more than USD 2.6 million per year. The resulting saving effect can recoup the costs of investing in a distributed energy resource system, including construction and installation of the local grid and automation infrastructure, and can be obtained in any country of the world.