Journal articles on the topic 'Load prediction and scheduling'
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1
Cheng, Qiangqiang, Yiqi Yan, Shichao Liu, Chunsheng Yang, Hicham Chaoui, and Mohamad Alzayed. "Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling." Energies 13, no. 24 (December 8, 2020): 6489. http://dx.doi.org/10.3390/en13246489.
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This paper proposes a particle filter (PF)-based electricity load prediction method to improve the accuracy of the microgrid day-ahead scheduling. While most of the existing prediction methods assume electricity loads follow normal distributions, we consider it is a nonlinear and non-Gaussian process which is closer to the reality. To handle the nonlinear and non-Gaussian characteristics of electricity load profile, the PF-based method is implemented to improve the prediction accuracy. These load predictions are used to provide the microgrid day-ahead scheduling. The impact of load prediction error on the scheduling decision is analyzed based on actual data. Comparison results on a distribution system show that the estimation precision of electricity load based on the PF method is the highest among several conventional intelligent methods such as the Elman neural network (ENN) and support vector machine (SVM). Furthermore, the impact of the different parameter settings are analyzed for the proposed PF based load prediction. The management efficiency of microgrid is significantly improved by using the PF method.
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Li, Fenglei, Chunxia Dou, and Shiyun Xu. "Optimal Scheduling Strategy of Distribution Network Based on Electric Vehicle Forecasting." Electronics 8, no. 7 (July 22, 2019): 816. http://dx.doi.org/10.3390/electronics8070816.
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Based on the Monte Carlo method, this paper simulates, predicts the load, and considers the travel chain of electric vehicles and different charging methods to establish a predictive model. Based on the results of electric vehicle simulation prediction, an optimal scheduling model of the distribution network considering the demand response side load is established. The firefly optimization algorithm is used to solve the optimal scheduling problem. The results show that the prediction model proposed in this paper has a certain reference value for the prediction of an electric vehicle load. The electric vehicle is placed in the optimal scheduling resource of the distribution network, which increases the dimension of the scheduling resources of the network and improves the economics of the distribution network operation.
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Wang, Kui, Bu Han Zhang, Jia Jun Zhai, Wen Shao, Xiao Shan Wu, and Cheng Xiong Mao. "Coordination between Short-Term and Real-Time Scheduling Incorporating Wind Power." Advanced Materials Research 512-515 (May 2012): 700–703. http://dx.doi.org/10.4028/www.scientific.net/amr.512-515.700.
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Coordination strategies between short-term (e.g., weekly and daily scheduling ) and real-time scheduling in wind power integrated system are disscussed. To cope with the uncertainty of wind power and load demands, weekly and daily rolling schedulings are applied. According to the latest updated prediction results of wind power and load demands, weekly rolling scheduling is applied to revise unit commitment and fuel allocation in remaining hours in a week. Daily rolling scheduling is applied to revise generation scheduling in remaining time in a day. A modified IEEE 118-bus system is applied to test the proposed approach.
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Li, Jingyun, and Hong Zhao. "Construction of an Optimal Scheduling Method for Campus Energy Systems Based on Deep Learning Models." Mathematical Problems in Engineering 2022 (March 31, 2022): 1–10. http://dx.doi.org/10.1155/2022/5350786.
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Aiming at the problem of high cost and low efficiency of planning and scheduling caused by load uncertainty of campus energy system, a 3-layer planning and scheduling model based on multivariate load prediction is proposed, mainly including prediction layer, planning layer, and scheduling layer; a long-term and short-term prediction model of multivariate load is constructed based on random forest regression network and long and short-term memory network. With the objective of minimizing the comprehensive planning and scheduling cost and the scheduling operation cost, the optimal comprehensive system cost and configuration scheme are obtained by using improved particle swarm algorithm and CPLEX solver; the equipment status and system cost are analyzed by planning and scheduling under different scenarios. By comparing the planning and scheduling results of the constructed 3-layer model with the conventional two-layer model, the economy and reliability of the 3-layer planning and scheduling model are demonstrated.
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Wang, Haiji, and Xueying Lu. "Research on short-term forecasting of power load based on big data BP neural network." Journal of Physics: Conference Series 2401, no. 1 (December 1, 2022): 012077. http://dx.doi.org/10.1088/1742-6596/2401/1/012077.
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Abstract The power network system is an indispensable part of the economy development, which directly affects the stable operation of various industries and people’s daily life. During the stable operation of the power system, the prediction of the power load plays an important role in the load scheduling of the power system. Aiming at the problem of short-term load forecasting of power system, this paper established a short-term forecasting model of power load based on the BP neural network forecasting model through the collection of big data and modified the network weights and thresholds through model training. Finally, a short-term prediction of the power load of a certain community was carried out. The results show that the prediction model based on BP neural network can accurately predict the short-term power load with small prediction errors and good prediction performance. It can meet the precision requirements of power system operation scheduling.
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Geary, Elizabeth M., Martin Goldberg, A. G. Greenburg, and Thomas E. Johnson. "Predicting operating room case load: An aid to resource allocation." Journal of Hospital Administration 2, no. 4 (August 26, 2013): 151. http://dx.doi.org/10.5430/jha.v2n4p151.
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Hospital patient bed utilization can reach 100% with an impact on elective surgery schedules. Analysis of the demand for beds created by elective surgical operations is desirable to manage overall resources under these conditions. For planning and allocating operating rooms, staff, beds and equipment on any given day, hospital administrators would benefit from an accurate prediction of the number of surgical cases that will be completed. Current scheduling techniques do not predict, for a given day in the future, the number of cases that will actually be performed. A study was performed at a 247 bed hospital with 10 operating rooms. The operating rooms were available for reservation more than two weeks in advance. Both block scheduling and open time were available. Using reservation data with a simple Black Box model allows the prediction of the total number of cases to be performed up to two weeks in advance with 90% accuracy. The resultant predictive demand should allow for better resource planning for the Operating Suite as well as required post-op hospital patient beds.
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Li, Zhengjie, and Zhisheng Zhang. "Day-Ahead and Intra-Day Optimal Scheduling of Integrated Energy System Considering Uncertainty of Source & Load Power Forecasting." Energies 14, no. 9 (April 28, 2021): 2539. http://dx.doi.org/10.3390/en14092539.
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At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the integrated energy system. In order to solve this problem, a day-ahead and intra-day optimal scheduling model of integrated energy system considering forecasting uncertainty is proposed in this paper, which takes the minimum operation cost of the system as the target, and different processing strategies are adopted for the model. In the day-ahead time scale, according to day-ahead load forecasting, an integrated demand response (IDR) strategy is formulated to adjust the load curve, and an optimal scheduling scheme is obtained. In the intra-day time scale, the predicted value of wind power, solar power and load power are represented by fuzzy parameters to participate in the optimal scheduling of the system, and the output of units is adjusted based on the day-ahead scheduling scheme according to the day-ahead forecasting results. The simulation of specific examples shows that the integrated demand response can effectively adjust the load demand and improve the economy and reliability of the system operation. At the same time, the operation cost of the system is related to the reliability of the accurate prediction of wind power, solar power and load power. Through this model, the optimal scheduling scheme can be determined under an acceptable prediction accuracy and confidence level.
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Lu, You Wei, Zhen Zhen Xu, and Feng Xia. "Prediction-Based Independent Task Scheduling for Heterogeneous Distributed Computing Systems." Advanced Materials Research 457-458 (January 2012): 1039–46. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.1039.
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Independent task scheduling algorithms in distributed computing systems deal with three main conflicting factors including load balance, task execution time and scheduling cost. In this paper, the problem of scheduling tasks arriving at a low rate and with long execution time in heterogeneous computing systems is studied, and a new scheduling algorithm based on prediction is proposed. This algorithm evaluates the utility of task scheduling based on statistics and prediction to solve the influence of heterogeneous computing systems. The experimental results reveal that the proposed algorithm adequately balances the conflicting factors, and thus performs better than some classical algorithms such as MCT and MET when the parameters are well selected.
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Jin, Xue-Bo, Wei-Zhen Zheng, Jian-Lei Kong, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, and Seng Lin. "Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization." Energies 14, no. 6 (March 13, 2021): 1596. http://dx.doi.org/10.3390/en14061596.
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Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.
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GAMBOA, CARLOS FERNANDO, and THOMAS ROBERTAZZI. "SIMPLE PERFORMANCE BOUNDS FOR MULTICORE AND PARALLEL CHANNEL SYSTEMS." Parallel Processing Letters 21, no. 04 (December 2011): 439–60. http://dx.doi.org/10.1142/s012962641100031x.
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A simple modification of existing divisible load scheduling algorithms, boosting link speed by M for M parallel channels per link, allows time optimal load scheduling and performance prediction for parallel channel systems. The situation for multicore models is more complex but can be handled by a substitution involving equivalent processor speed. These modifications yield upper bounds on such parallel systems' performance. This concept is illustrated for ideal single level (star) tree networks under a variety of scheduling policies. Less than ideal parallelism can also be modeled though mechanisms of inefficiency require further research.
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Tian, W., and H. P. Zhang. "A dynamic job-shop scheduling model based on deep learning." Advances in Production Engineering & Management 16, no. 1 (March 26, 2021): 23–36. http://dx.doi.org/10.14743/apem2021.1.382.
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Ideally, the solution to job-shop scheduling problem (JSP) should effectively reduce the cost of manpower and materials, thereby enhancing the core competitiveness of the manufacturer. Deep learning (DL) neural networks have certain advantages in handling complex dynamic JSPs with a massive amount of historical data. Therefore, this paper proposes a dynamic job-shop scheduling model based on DL. Firstly, a data prediction model was established for dynamic job-shop scheduling, with long short-term memory network (LSTM) as the basis; the Dropout technology and adaptive moment estimation (ADAM) were introduced to enhance the generalization ability and prediction effect of the model. Next, the dynamic JSP was described in details, and three objective functions, namely, maximum makespan, total device load, and key device load, were chosen for optimization. Finally, the multi-objective problem of dynamic JSP scheduling was solved by the improved multi-objective genetic algorithm (MOGA). The effectiveness of the algorithm was proved experimentally.
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Yu, Jing, Feng Ding, Chenghao Guo, and Yabin Wang. "System load trend prediction method based on IF-EMD-LSTM." International Journal of Distributed Sensor Networks 15, no. 8 (August 2019): 155014771986765. http://dx.doi.org/10.1177/1550147719867655.
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Accurately predicting the load change of the information system during operation has important guiding significance for ensuring that the system operation is not interrupted and resource scheduling is carried out in advance. For the information system monitoring time series data, this article proposes a load trend prediction method based on isolated forests-empirical modal decomposition-long-term (IF-EMD-LSTM). First, considering the problem of noise and abnormal points in the original data, the isolated forest algorithm is used to eliminate the abnormal points in the data. Second, in order to further improve the prediction accuracy, the empirical modal decomposition algorithm is used to decompose the input data into intrinsic mode function (IMF) components of different frequencies. Each intrinsic mode function (IMF) and residual is predicted using a separate long-term and short-term memory neural network, and the predicted values are reconstructed from each long-term and short-term memory model. Finally, experimental verification was carried out on Amazon’s public data set and compared with autoregressive integrated moving average and Prophet models. The experimental results show the superior performance of the proposed IF-EMD-LSTM prediction model in information system load trend prediction.
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Ünal, Fatih, Abdulaziz Almalaq, and Sami Ekici. "A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning." Applied Sciences 11, no. 6 (March 18, 2021): 2742. http://dx.doi.org/10.3390/app11062742.
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Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.
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Yan, Guoqiang, Shaojie Mao, and Yuping Li. "Resource Allocation for Cloud-Edge-End Simulation System." Journal of Physics: Conference Series 2173, no. 1 (January 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2173/1/012007.
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Abstract Aiming at the problem of simulating service requests and optimal scheduling during the operation of the cloud computing/edge computing environment, the real-time optimization scheduling technology of computing resources is studied, and elastic resource optimization scheduling is realized through data feature mining analysis, service load prediction, and collaborative resource management. Ensure that the simulation service quality meets the mission requirements and provide support.
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Zheng, Guilin, and Li Zhang. "The Electrical Load Forecasting Base on an Optimal Selection Method of Multiple Models in DSM." International Journal of Online Engineering (iJOE) 11, no. 8 (October 26, 2015): 34. http://dx.doi.org/10.3991/ijoe.v11i8.4882.
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Electrical load forecasting plays a key role in energy scheduling and planning. It is a challenge to predict electric load accurately due to the versatility of electrical loads and the vast number of users in DSM of low-voltage side. Most of electrical load forecasting research focused on single model prediction or combination model prediction, which cannot get the optimal performance for some cases. Therefore, how to gather maximum optimal information from various different models is a key point in load forecasting and analysis. In this paper, an optimal selection method of multiple models for electrical load forecasting is studied. This method overcomes the shortcoming of unitary model, such as the instability and poor accuracy in some cases. To evaluate the forecast performance, a practical case is studied based on the intelligent electricity management system, which is presented by Wuhan University. It can be seen that the prediction error of the forecasting models can be calculated automatically and final optimum model can be obtained by optimum seeking software platform.
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R, Anitha, and C. Vidya Raj. "A Comprehensive Survey on SLA Compliant Energy Aware Resource Allocation in Cloud Datacenters." International Journal of Emerging Research in Management and Technology 6, no. 6 (June 29, 2018): 335. http://dx.doi.org/10.23956/ijermt.v6i6.290.
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Cloud Computing has achieved immense popularity due to its unmatched benefits and characteristics. With its increasing popularity and round the clock demand, cloud based data centers often suffer with problems due to over-usage of resources or under-usage of capable servers that ultimately leads to wastage of energy and overall elevated cost of operation. Virtualization plays a key role in providing cost effective solution to service users. But on datacenters, load balancing and scheduling techniques remain inevitable to provide better Quality of Service to the service users and maintenance of energy efficient operations in datacenters. Energy-Aware resource allocation and job scheduling mechanisms in VMs has helped datacenter providers to reduce their cost incurrence through predictive job scheduling and load balancing. But it is quite difficult for any SLA oriented systems to maintain equilibrium between QoS and cost incurrence while considering their legal assurance of quality, as there should not be any violations in their service agreement. This paper presents some state-of-the-art works by various researchers and experts in the arena of cloud computing systems and particularly emphasizes on energy aware resource allocations, job scheduling techniques, load balancing and price prediction methods. Comparisons are made to demonstrate usefulness of the mechanisms in different scenarios.
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Park, Minsu, Jaehwi Kim, Dongjun Won, and Jaehee Kim. "Development of a Two-Stage ESS-Scheduling Model for Cost Minimization Using Machine Learning-Based Load Prediction Techniques." Processes 7, no. 6 (June 12, 2019): 370. http://dx.doi.org/10.3390/pr7060370.
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Effective use of energy storage systems (ESS) is important to reduce unnecessary power consumption. In this paper, a day-ahead two-stage ESS-scheduling model based on the use of a machine learning technique for load prediction has been proposed for minimizing the operating cost of the energy system. The proposed algorithm consists of two stages of ESS. In the first stage, ESS is used to minimize demand charges by reducing the peak load. Then, the remaining capacity is used to reduce energy charges through arbitrage trading, thereby minimizing the total operating cost. To achieve this purpose, accurate load prediction is required. Machine learning techniques are promising methods owing to the ability to improve forecasting performance. Among them, ensemble learning is a well-known machine learning method which helps to reduce variance and prevent overfitting of a model. To predict loads, we employed bootstrap aggregating (bagging) or random forest technique-based decision trees after Holt–Winters smoothing for trends. Our combined method can increase the prediction accuracy. In the simulation conducted, three combined prediction models were evaluated. The prediction task was performed using the R programming language. The effectiveness of the proposed algorithm was verified by using Python’s PuLP library.
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Dalle Ave, Giancarlo, Iiro Harjunkoski, and Sebastian Engell. "A non-uniform grid approach for scheduling considering electricity load tracking and future load prediction." Computers & Chemical Engineering 129 (October 2019): 106506. http://dx.doi.org/10.1016/j.compchemeng.2019.06.031.
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Tan, Min, Ruixuan Ba, and Guohui Li. "Data Center Traffic Prediction Algorithms and Resource Scheduling." Sensors 22, no. 20 (October 17, 2022): 7893. http://dx.doi.org/10.3390/s22207893.
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This paper uses intelligent methods such as a time recurrent neural network to predict network traffic, mainly to solve the problems of resource imbalance and demand differentiation under the current 5G cloud-network collaborative architecture. An improved tree species optimization algorithm is proposed to optimize the initial network data, and the LSTM model is used to predict the data center traffic to obtain better network traffic prediction accuracy, take corresponding measures, and finally build a scheduling algorithm that integrates business cooperative caching and load balancing based on traffic prediction to reduce the peak pressure of the 5G data center network.
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Aoyang, Han, Yu Litao, An Shuhuai, and Zhang Zhisheng. "Short-term Load Forecasting Model for Microgrid Based on HSA-SVM." MATEC Web of Conferences 173 (2018): 01007. http://dx.doi.org/10.1051/matecconf/201817301007.
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Short-term load forecasting for microgrid is the basis of the research on scheduling techniques of microgrid. Accurate load forecasting for microgrid will provide the necessary basis for cooperative optimization scheduling. Short-term loadforecasting model for microgrid based on support vector machine(SVM) is constructed in this paper. The harmony search optimization algorithm(HSA) is used to optimize the parameters of the SVM model, because it has the advantages of fast convergence speed and better optimization ability. Through the simulation and test of the actual microgrid load system, it is proved that the short-term loadforecasting model for microgrid based on HSA-SVM can effectively improve the prediction accuracy.
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Hwang, Jin Sol, Ismi Rosyiana Fitri, Jung-Su Kim, and Hwachang Song. "Optimal ESS Scheduling for Peak Shaving of Building Energy Using Accuracy-Enhanced Load Forecast." Energies 13, no. 21 (October 28, 2020): 5633. http://dx.doi.org/10.3390/en13215633.
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This paper proposes an optimal Energy Storage System (ESS) scheduling algorithm Building Energy Management System (BEMS). In particular, the focus is placed on how to reduce the peak load using ESS and load forecast. To this end, first, an existing deep learning-based load forecast method is applied to a real building energy prediction and it is shown that the deep learning-based method leads to an accuracy-enhanced load forecast. Second, an optimization problem is formulated in order to devise an ESS scheduling. In the optimization problem, the objective function and constraints are defined such that the peak load is reduced; the cost for electricity is minimized; and the ESS’s lifetime is elongated considering the accuracy-enhanced load forecast, real-time electricity price, and the state-of-charge of the ESS. For the purpose of demonstrating the effectiveness of the proposed ESS scheduling method, it is implemented using a real building load power and temperature data. The simulation results show that the proposed method can reduce the peak load and results in smooth charging and discharging, which is important for the ESS lifetime.
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Liu, Yazhi, Jiye Zhang, Wei Li, Qianqian Wu, and Pengmiao Li. "Load Balancing Oriented Predictive Routing Algorithm for Data Center Networks." Future Internet 13, no. 2 (February 22, 2021): 54. http://dx.doi.org/10.3390/fi13020054.
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A data center undertakes increasing background services of various applications, and the data flows transmitted between the nodes in data center networks (DCNs) are consequently increased. At the same time, the traffic of each link in a DCN changes dynamically over time. Flow scheduling algorithms can improve the distribution of data flows among the network links so as to improve the balance of link loads in a DCN. However, most current load balancing works achieve flow scheduling decisions to the current links on the basis of past link flow conditions. This situation impedes the existing link scheduling methods from implementing optimal decisions for scheduling data flows among the network links in a DCN. This paper proposes a predictive link load balance routing algorithm for a DCN based on residual networks (ResNet), i.e., the link load balance route (LLBR) algorithm. The LLBR algorithm predicts the occupancy of the network links in the next duty cycle, according to the ResNet architecture, and then the optimal traffic route is selected according to the predictive network environment. The LLBR algorithm, round-robin scheduling (RRS), and weighted round-robin scheduling (WRRS) are used in the same experimental environment. Experimental results show that compared with the WRRS and RRS, the LLBR algorithm can reduce the transmission time by approximately 50%, reduce the packet loss rate from 0.05% to 0.02%, and improve the bandwidth utilization by 30%.
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Nie, W., N. Xiong, and H. Wang. "Low-overhead uplink scheduling through load prediction for WiMAX real-time services." IET Communications 5, no. 8 (May 20, 2011): 1060–67. http://dx.doi.org/10.1049/iet-com.2010.0770.
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Shen, Wenfeng, Zhaokai Luo, Daming Wei, Weimin Xu, and Xin Zhu. "Load-prediction scheduling algorithm for computer simulation of electrocardiogram in hybrid environments." Journal of Systems and Software 102 (April 2015): 182–91. http://dx.doi.org/10.1016/j.jss.2015.01.015.
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Gong, Gangjun, Xiaonan An, Nawaraj Kumar Mahato, Shuyan Sun, Si Chen, and Yafeng Wen. "Research on Short-Term Load Prediction Based on Seq2seq Model." Energies 12, no. 16 (August 20, 2019): 3199. http://dx.doi.org/10.3390/en12163199.
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Electricity load prediction is the primary basis on which power-related departments to make logical and effective generation plans and scientific scheduling plans for the most effective power utilization. The perpetual evolution of deep learning has recommended advanced and innovative concepts for short-term load prediction. Taking into consideration the time and nonlinear characteristics of power system load data and further considering the impact of historical and future information on the current state, this paper proposes a Seq2seq short-term load prediction model based on a long short-term memory network (LSTM). Firstly, the periodic fluctuation characteristics of users’ load data are analyzed, establishing a correlation of the load data so as to determine the model’s order in the time series. Secondly, the specifications of the Seq2seq model are given preference and a coalescence of the Residual mechanism (Residual) and the two Attention mechanisms (Attention) is developed. Then, comparing the predictive performance of the model under different types of Attention mechanism, this paper finally adopts the Seq2seq short-term load prediction model of Residual LSTM and the Bahdanau Attention mechanism. Eventually, the prediction model obtains better results when merging the actual power system load data of a certain place. In order to validate the developed model, the Seq2seq was compared with recurrent neural network (RNN), LSTM, and gated recurrent unit (GRU) algorithms. Last but not least, the performance indices were calculated. when training and testing the model with power system load data, it was noted that the root mean square error (RMSE) of Seq2seq was decreased by 6.61%, 16.95%, and 7.80% compared with RNN, LSTM, and GRU, respectively. In addition, a supplementary case study was carried out using data for a small power system considering different weather conditions and user behaviors in order to confirm the applicability and stability of the proposed model. The Seq2seq model for short-term load prediction can be reported to demonstrate superiority in all areas, exhibiting better prediction and stable performance.
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Yudantaka, Khikmafaris, Jung-Su Kim, and Hwachang Song. "Dual Deep Learning Networks Based Load Forecasting with Partial Real-Time Information and Its Application to System Marginal Price Prediction." Energies 13, no. 1 (December 27, 2019): 148. http://dx.doi.org/10.3390/en13010148.
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Load power forecast is one of most important tasks in power systems operation and maintenance. Enhancing its accuracy can be helpful to power systems scheduling. This paper presents how to use partial real-time temperature information in forecasting load power, which is usually done using past load power and temperature data. The partial real-time temperature information means temperature information for only part of the entire prediction time interval. To this end, a long short-term memory (LSTM) network is trained using past temperature and load power data in order to forecast load power, where forecasted load power depends on the temperature prediction implicitly. Then, in order to deal with the case where nontrivial temperature prediction errors happen, a multi-layer perceptron (MLP) network is trained using the past data describing the relation between temperature variation and load power variation. Then, the temperature is measured at the beginning of the prediction time-interval and compensated load forecast is computed by adding the output of the LSTM and that of the MLP whose input is the temperature prediction error. It is shown that the proposed compensation using the real-time temperature information indeed improves performance of load power forecast. This improved load forecast is used to predict system marginal price (SMP). The proposed method is validated using the real temperature and load power data of South Korea.
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Zheng, Chaoran, Mohsen Eskandari, Ming Li, and Zeyue Sun. "GA−Reinforced Deep Neural Network for Net Electric Load Forecasting in Microgrids with Renewable Energy Resources for Scheduling Battery Energy Storage Systems." Algorithms 15, no. 10 (September 21, 2022): 338. http://dx.doi.org/10.3390/a15100338.
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The large−scale integration of wind power and PV cells into electric grids alleviates the problem of an energy crisis. However, this is also responsible for technical and management problems in the power grid, such as power fluctuation, scheduling difficulties, and reliability reduction. The microgrid concept has been proposed to locally control and manage a cluster of local distributed energy resources (DERs) and loads. If the net load power can be accurately predicted, it is possible to schedule/optimize the operation of battery energy storage systems (BESSs) through economic dispatch to cover intermittent renewables. However, the load curve of the microgrid is highly affected by various external factors, resulting in large fluctuations, which makes the prediction problematic. This paper predicts the net electric load of the microgrid using a deep neural network to realize a reliable power supply as well as reduce the cost of power generation. Considering that the backpropagation (BP) neural network has a good approximation effect as well as a strong adaptation ability, the load prediction model of the BP deep neural network is established. However, there are some defects in the BP neural network, such as the prediction effect, which is not precise enough and easily falls into a locally optimal solution. Hence, a genetic algorithm (GA)−reinforced deep neural network is introduced. By optimizing the weight and threshold of the BP network, the deficiency of the BP neural network algorithm is improved so that the prediction effect is realized and optimized. The results reveal that the error reduction in the mean square error (MSE) of the GA–BP neural network prediction is 2.0221, which is significantly smaller than the 30.3493 of the BP neural network prediction. Additionally, the error reduction is 93.3%. The error reductions of the root mean square error (RMSE) and mean absolute error (MAE) are 74.18% and 51.2%, respectively.
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Liu, Jicheng, and Yu Yin. "Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China." Energies 15, no. 3 (February 8, 2022): 1236. http://dx.doi.org/10.3390/en15031236.
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In order to implement the national need for the optimal allocation of power resources, power load forecasting, as an important research topic, has important theoretical and practical significance. The purpose of this study is to construct a prediction model considering climate factors based on a large amount of historical data, and to prove that the prediction accuracy is related to both climate factors and load regularity. The results of load forecasting are affected by many climate factors, so firstly the climate variables affecting load forecasting are screened. Secondly, a load prediction model based on the IPSO-Elman network learning algorithm is constructed by taking the difference between the predicted value of the neural network and the actual value as the fitness function of particle swarm optimization. In view of the great influence of weights and thresholds on the prediction accuracy of the Elman neural network, the particle swarm optimization algorithm (PSO) is used to optimize parameters in order to improve the prediction accuracy of ELMAN neural network. Thirdly, prediction with and without climate factors is compared and analyzed, and the prediction accuracy of the model compared by using cosine distance and various error indicators. Finally, the stability discriminant index of historical load regularity is introduced to prove that the accuracy of the prediction model is related to the regularity of historical load in the forecast area. The prediction method proposed in this paper can provide reference for power system scheduling.
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Yin, Yanlei, Lihua Wang, Jun Tang, Wanda Zhang, and Hongwei Niu. "An Optimal Scheduling Method for Data Resources of Production Process Based on Multicommunity Collaborative Search Algorithm." Journal of Sensors 2022 (March 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/2660462.
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Aiming at the problem of low response speed and unbalanced distribution of data resources of production process (DRPP) for the distributed workshop production environment, an optimization scheduling method of DRPP based on a multicommunity cooperative search algorithm is proposed. A heuristic data resource service scheduling framework including a load manager and dynamic scheduling engine is first built to deal with the uncertainty of data resource service response and the imbalance of resource allocation; a core scheduling optimization mathematical model with the objectives: resource service efficiency, reduced response time, and load balancing, is established. Then, a multicommunity cooperative search algorithm for the scheduling model is presented, and the mapping relationship between the particle position vector and resource allocation is established via binary coding. Thus, the optimization algorithm is mapped to discrete data space, and the multicommunity bidirectional driving evolutionary mechanism is used to realize the cooperative and interactive search between common and model community, which enhances the adaptability of the algorithm to dynamic random scheduling tasks. Finally, the effectiveness of the proposed method is verified by an example of multiprocess quality prediction service scheduling in silk production process, which provides an effective means for solving the complex scheduling problem of production process data.
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Yan, Lijie, and Xudong Liu. "The predicted load balancing algorithm based on the dynamic exponential smoothing." Open Physics 18, no. 1 (August 14, 2020): 439–47. http://dx.doi.org/10.1515/phys-2020-0108.
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AbstractTo a large extent, the load balancing algorithm affects the clustering performance of the computer. This paper illustrated the common load balancing algorithms and elaborated on the advantages and drawbacks of such algorithms. In addition, this paper provides a kind of balancing algorithm generated on the basis of the load prediction. Due to the dynamic exponential smoothing model, such an algorithm helps obtain the corresponding smoothing coefficient with the server node load time series of current phrase and allows researchers to make prediction with the load value at the next moment of this node. Subsequently, the dispatcher makes the scheduling with the serve request of users according to the load predicted value. OPNET Internet simulated software is applied to the test, and we may conclude from the results that the application of such an algorithm acquires a higher load balancing efficiency and better load balancing effect.
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He, Huihui, Shengjun Huang, Yajie Liu, and Tao Zhang. "Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction." Discrete Dynamics in Nature and Society 2021 (October 14, 2021): 1–14. http://dx.doi.org/10.1155/2021/2198846.
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With the integration of Renewable Energy Resources (RERs), the Day-Ahead (DA) scheduling for the optimal operation of the integrated Isolated Microgrids (IMGs) may not be economically optimal in real time due to the prediction errors of multiple uncertainty sources. To compensate for prediction error, this paper proposes a Robust Model Predictive Control (RMPC) based on an interval prediction approach to optimize the real-time operation of the IMGs, which diminishes the influence from prediction error. The rolling optimization model in RMPC is formulated into the robust model to schedule operation with the consideration of the price of robustness. In addition, an Online Learning (OL) method for interval prediction is utilized in RMPC to predict the future information of the uncertainties of RERs and load, thereby limiting the uncertainty. A case study demonstrates the effectiveness of the proposed with the better matching between demand and supply compared with the traditional Model Predictive Control (MPC) method and Hard Charging (HC) method.
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He, Wei, Jia Li, Weizhe Zhao, and Yaqing Zhang. "Optimal scheduling method based on building virtual energy storage equivalent battery." E3S Web of Conferences 252 (2021): 03001. http://dx.doi.org/10.1051/e3sconf/202125203001.
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In this paper, an optimal scheduling method based on building virtual energy storage equivalent battery is proposed. Firstly, the thermal load prediction model is built based on the thermodynamic model, and then the equivalent battery model of virtual energy storage is established by combining with inverter air conditioning system model. Then, with the goal of minimizing the user’s electricity cost, considering the constraints of energy storage state and charging and discharging power of equivalent battery model, an optimal scheduling method of building virtual energy storage equivalent battery is constructed. The results show that the proposed method can predict the building thermal load, update the building virtual energy storage equivalent battery parameters, formulate the corresponding optimal scheduling strategy, and reduce the electricity cost of users for heating on the premise of ensuring the thermal comfort of users.
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Shang, Wenqian, Di Liu, Ligu Zhu, and Dongyu Feng. "An Improved Dynamic Load-Balancing Model." International Journal of Software Innovation 5, no. 3 (July 2017): 33–48. http://dx.doi.org/10.4018/ijsi.2017070103.
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With the rapid development of big data and the national big data strategy is put forward, the Web server cluster facing more complex and severe challenges. The traditional load balancing algorithm has obvious limitations. This paper proposes a dynamic load-balancing model based on the SSAWF (Strong Suspend And Weak Forecast) mechanism. This model uses strong suspend mechanism and cubic exponential smoothing prediction method based on AHP algorithm for dynamic load balancing scheduling. Results of the experiments show that the improved model has more positive influence on read/ write performance of the cluster under abnormal system transient performance, high concurrency and high system load interaction, that's to say the load balancing effect is better than the traditional load balancing.
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Teekaraman, Yuvaraja, K. A. Ramesh Kumar, Ramya Kuppusamy, and Amruth Ramesh Thelkar. "SSNN-Based Energy Management Strategy in Grid Connected System for Load Scheduling and Load Sharing." Mathematical Problems in Engineering 2022 (January 10, 2022): 1–9. http://dx.doi.org/10.1155/2022/2447299.
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The proposed research work focused on energy management strategy (EMS) in a grid connected system working in islanding mode with the connected renewable energy resources and battery storage system. The energy management strategy developed provides a balancing operation at its output by utilizing perfect load sharing strategy. The EMS technique using smart superficial neural network (SSNN) is simulated, and numerical analyses are presented to validate the effectiveness of the centralized energy management strategy in a grid connected islanded system. A SSNN prediction model is unified to forecast the associated household load demand, PV generation system under various time horizons (including the disaster condition), EV availability, and status on EV section and distance. SSNN is one the most reliable forecasting methods in many of the applications. The developed system is also accounted for degradation battery model and its associated cost. The incorporation of energy management strategy (EMS) reduces the amount of energy drawn from the grid connected system when compared with the other optimized systems.
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Tayab, Usman Bashir, Junwei Lu, Seyedfoad Taghizadeh, Ahmed Sayed M. Metwally, and Muhammad Kashif. "Microgrid Energy Management System for Residential Microgrid Using an Ensemble Forecasting Strategy and Grey Wolf Optimization." Energies 14, no. 24 (December 16, 2021): 8489. http://dx.doi.org/10.3390/en14248489.
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Microgrid (MG) is a small-scale grid that consists of multiple distributed energy resources and load demand. The microgrid energy management system (M-EMS) is the decision-making centre of the MG. An M-EMS is composed of four modules which are known as forecasting, scheduling, data acquisition, and human-machine interface. However, the forecasting and scheduling modules are considered the major modules from among the four of them. Therefore, this paper proposed an advanced microgrid energy management system (M-EMS) for grid-connected residential microgrid (MG) based on an ensemble forecasting strategy and grey wolf optimization (GWO) based scheduling strategy. In the forecasting module of M-EMS, the ensemble forecasting strategy is proposed to perform the short-term forecasting of PV power and load demand. The GWO based scheduling strategy has been proposed in scheduling module of M-EMS to minimize the operating cost of grid-connected residential MG. A small-scale experiment is conducted using Raspberry Pi 3 B+ via the python programming language to validate the effectiveness of the proposed M-EMS and real-time historical data of PV power, load demand, and weather is adopted as inputs. The performance of the proposed forecasting strategy is compared with ensemble forecasting strategy-1, particle swarm optimization based artificial neural network, and back-propagation neural network. The experimental results highlight that the proposed forecasting strategy outperforms the other strategies and achieved the lowest average value of normalized root mean square error of day-ahead prediction of PV power and load demand for the chosen day. Similarly, the performance of GWO based scheduling strategy of M-EMS is analyzed and compared for three different scenarios. Finally, the experimental results prove the outstanding performance of the proposed scheduling strategy.
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Balagoni, Yadaiah, and R. Rajeswara Rao. "Locality-Load-Prediction Aware Multi-Objective Task Scheduling in the Heterogeneous Cloud Environment." Indian Journal of Science and Technology 10, no. 9 (February 1, 2017): 1–9. http://dx.doi.org/10.17485/ijst/2017/v10i9/106576.
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Li, X. B., L. Dan, and Q. Wu. "An Epon Dynamic Bandwidth Allocation Algorithm Based on the Least Mean Square Prediction." Advanced Materials Research 216 (March 2011): 574–78. http://dx.doi.org/10.4028/www.scientific.net/amr.216.574.
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To ensure different priorities of different services, for instance the voice, video and data, EPON dynamic bandwidth allocation algorithm must be able to accurately predict frames between the two authorizations. In respect to this problem,this paper presents a multi-queue scheduling EPON dynamic bandwidth allocation algorithm on the basis of the least mean square prediction and illustrates the specific algorithm as well as the algorithm simulation. Simulation results show that this scheme can predict the frames of high-priority services and reasonably allocates bandwidth based on the predictions. Thus the scheme fundamentally avoids light load penalty and reduces the delay of high priority services.
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Zhao, Luo, Xinan Zhang, Yifu Chen, Xiuyan Peng, and Yankai Cao. "An Improved Load Forecasting Method Based on the Transfer Learning Structure under Cyber-Threat Condition." Computational Intelligence and Neuroscience 2022 (August 24, 2022): 1–15. http://dx.doi.org/10.1155/2022/1696663.
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Smart grid is regarded as an evolutionary regime of existing power grids. It integrates artificial intelligence and communication technologies to fundamentally improve the efficiency and reliability of power systems. One serious challenge for the smart grid is its vulnerability to cyber threats. In the event of a cyber attack, grid data may be missing; subsequently, load forecast and power planning that rely on these data cannot be processed by generation centers. To address this issue, this paper proposes a transfer learning-based framework for smart grid scheduling that is less reliant on local data while capable of delivering schedules with low operating cost. Specifically, the proposed framework contains (1) a power forecasting model based on transfer learning which can provide high quality load prediction with limited training data, (2) a novel adaptive time series prediction method with modeling time series from a covariate shift perspective that aims to train the forecasting model with a strong generalization capability, and (3) a day-ahead optimal economic power scheduling model considering a shared energy storage station.
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Han, Shi-Yuan, Qi-Wei Sun, Xiao-Hui Yang, Rui-Zhi Han, Jin Zhou, and Yue-Hui Chen. "Adaptation Scheduling for Urban Traffic Lights via FNT-Based Prediction of Traffic Flow." Electronics 11, no. 4 (February 20, 2022): 658. http://dx.doi.org/10.3390/electronics11040658.
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By linking computational intelligence technology directly to urban transportation systems, a framework for scheduling traffic lights is proposed to enhance their flexibility in adaptation to traffic fluctuation. First, based on the flexible neural tree (FNT) theory, an algorithm for predicting the traffic flow is designed to obtain the variance tendency of traffic load. After that, a strategy for adjusting the duration of traffic signal cycle is designed to tackle the problem of overload or lightweight traffic flow in the next-time frame. While predetermining the duration of signal cycle in the next-time frame, from a utilization perspective, an elastic-adaption strategy for scheduling the separate phase’s green traffic lights is derived from the analytical solution, which is obtained from a designed trade-off scheduling optimization problem to increase the adaptability for the upcoming traffic flow. The experiment results show that the proposed framework can effectively reduce the delay and stopping rate of vehicles, and improves the adaptability for the upcoming traffic flow.
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Yuwei, Cao, Zeng Ming, Jiang Shigong, Yang Weihong, Shi Pengjia, and Guo Xiaopeng. "Short-term Forecast of Multiple Loads in Integrated Energy System Based on IPSO-WNN." E3S Web of Conferences 194 (2020): 01029. http://dx.doi.org/10.1051/e3sconf/202019401029.
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Accurate short-term energy load forecasting has a considerable influence on the economic scheduling and optimal operation of integrated energy system. This study proposes an improved particle swarm optimization-wavelet neural network (IPSO-WNN) method for short-term load forecasting of integrated energy system. First, Kendall rank correlation coefficient in Copula theory is used to analyze the correlation among the influencing factors, through which the influencing factors with strong correlation are selected as input variables of the model. Secondly, chaos algorithm and adaptive weight selection strategy are introduced in the POS-WNN forecasting model to improve the prediction accuracy. Therefore, a short-term load forecasting model of integrated energy system based on IPSO-WNN is established. Finally, the analysis of examples shows that the load prediction accuracy is significantly improved based on the IPSO-WNN model compared with the traditional forecasting model.
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Gao, Wanli. "Intelligent Prediction Algorithm of Cross-Border E-Commerce Logistics Cost Based on Cloud Computing." Scientific Programming 2021 (October 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/7038294.
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Cross-border e-commerce logistics cost prediction algorithm does not consider logistics distribution scheduling, and logistics information interchange is not enough, which leads to confusion of logistics cost parameters and large deviation. Therefore, an intelligent prediction algorithm of cross-border e-commerce logistics cost based on cloud computing is designed. Introduce cloud computing platforms, optimize the scheduling of cross-border e-commerce logistics distribution tasks, and select the targets for the scheduling of cross-border e-commerce logistics distribution tasks from the aspects such as the shortest waiting time required by customers, the degree of resource load balance, and the costs consumed in completing cross-border e-commerce logistics distribution tasks, and design logistics scheduling process. On this basis, the logistics distribution data are classified, the association rules between the data are mined, and the monitoring of abnormal values in the cost forecasting process is completed. In order to eliminate the interference caused by the difference of different cost management interval, the function value is calculated by weighted Euclidean distance. Design feedback forecast mechanism to realize intelligent forecast algorithm of cross-border e-commerce logistics cost. Experimental results show that the proposed algorithm has better accuracy of cross-border e-commerce logistics cost prediction and higher completion rate of logistics tasks.
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Wang, Xin, Zhijun Shang, Changqing Xia, Shijie Cui, and Shuai Shao. "TSN Switch Queue Length Prediction Based on an Improved LSTM Network." Wireless Communications and Mobile Computing 2021 (December 22, 2021): 1–13. http://dx.doi.org/10.1155/2021/5130888.
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With the high-speed development of network technology, time-sensitive networks (TSNs) are experiencing a phase of significant traffic growth. At the same time, they have to ensure that highly critical time-sensitive information can be transmitted in a timely and accurate manner. In the future, TSNs will have to further improve network throughput to meet the increasing traffic demand based on the guaranteed transmission delay. Therefore, an efficient route scheduling scheme is necessary to achieve network load balance and improve network throughput. A time-sensitive software-defined network (TSSDN) can address the highly distributed industrial Internet network infrastructure, which cannot be accomplished by traditional industrial communication technologies, and it can achieve distributed intelligent dynamic route scheduling of the network through global network monitoring. The prerequisite for intelligent dynamic scheduling is that the queue length of future switches can be accurately predicted so that dynamic route planning for flow can be performed based on the prediction results. To address the queue length prediction problem, we propose a TSN switch queue length prediction model based on the TSSDN architecture. The prediction process has three steps: network topology dimension reduction, feature selection, and training prediction. The principal component analysis (PCA) algorithm is used to reduce the dimensionality of the network topology to eliminate unnecessary redundancy and overlap of relevant information. Feature selection requires comprehensive consideration of the influencing factors that affect the switch queue length, such as time and network topology. The training prediction is performed with the help of our enhanced long short-term memory (LSTM) network. The input-output structure of the network is changed based on the extracted features to improve the prediction accuracy, thus predicting the network congestion caused by bursty traffic. Finally, the results of the simulation demonstrate that our proposed TSN switch queue length prediction model based on the improved LSTM network algorithm doubles the prediction accuracy compared to the original model because it considers more influencing factors as features in the neural network for training and learning.
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Hu, Xiao Xi, and Xian Wei Zhou. "Improved Ant Colony Algorithm on Scheduling Optimization of Cloud Computing Resources." Applied Mechanics and Materials 678 (October 2014): 75–78. http://dx.doi.org/10.4028/www.scientific.net/amm.678.75.
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To address the problem of high occupancy of resources and slow response of current scheduling of cloud computing resources, this paper proposes a scheduling optimization algorithm based improved ant colony algorithm. It makes resource reservation through migration of virtual machine, uses dynamic trend prediction algorithm to forecast the load changes of data center, and puts forward the concrete complement to adjust reduction. Experiments show that the combination algorithm proposed in this paper are efficient to improve the performance of data center, accelerate the response speed and increase the precision.
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Shi, Li Yan. "Energy-Saving Scheduling Algorithm of Public Transport Bus Based on Passenger Flow Distribution in Different Time." Applied Mechanics and Materials 713-715 (January 2015): 1703–7. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1703.
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The bus scheduling algorithm is researched and the algorithm is improved, the traditional bus scheduling algorithm has not fully considered the time-sharing distribution of passengers, the shift scheduling is not reasonable, resulted in the waste of resources, and the passenger satisfaction rate is low. An improved bus scheduling algorithm is proposed based on time-sharing passenger flow distribution and statistical prediction. According to the past period of time, the statistics record data are collected, and the passenger flow of each period and each line of the station are calculated. The required number of buses of each period time is determined, the optimal solution of energy saving buses scheduling result is obtained. The experimental results show that, the new method can consider the passenger flow distribution of each time, and it can greatly improve the full-load ratio of bus, the bus scheduling optimal scheme is obtained, it has good application value in practice.
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Zhang, Tieyan, Zongjun Yao, Jingwei Hu, and Jinfeng Huang. "Multi-Time Scale Rolling Optimization Scheduling of “Nealy-Zero Carbon Park” Based on Stepped Carbon Allowance Trading." International Transactions on Electrical Energy Systems 2022 (September 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/4449515.
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Aiming at the self-government capacity and multi-time scale energy regulation requirements of “Nearly-zero Carbon Park” (NZCP) under the background of “dual carbon goals” and energy Internet, a day-ahead-intraday rolling optimization scheduling method for NZCP based on stepped carbon allowance trading is proposed. First, the energy supply and demand characteristics of liquid-storage Carbon Capture Gas-fired Power Plants (CCGPP) and Power-to-Gas (P2G) equipment are studied, and a combined system model of CCGPP and P2G is established that takes into account the low-carbon emission requirements of NZCP, and waste pyrolysis power generation facilities and manure treatment facilities are introduced to form a Waste Utilization system (WU) to provide energy support for the power grid and gas network. Second, the carbon allowance offset and low-carbon benefit gains of NZCP are considered, a compensation coefficient is introduced to guide the low-carbon behavior of carbon emitters, and a ladder carbon allowance trading model is established. Then, the influence of the source-load prediction error on the optimal scheduling at different time scales is considered, and a two-stage unit output plan is established. Then, the influence of the source-load prediction error on the optimal scheduling at different time scales is considered, and a two-stage unit output adjustment plan is established. The calculation example results verify that the proposed day-ahead-intraday rolling optimization scheduling model for NZCP can effectively reduce system carbon emissions while reducing system operating costs, and the efficient integration of economic and environmental benefits is achieved.
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S. H., Joharry. "LOAD SCHEDULING FOR SMART HOME USING DAY-AHEAD PREDICTION FROM ARTIFICIAL NEURAL NETWORK (ANN)." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 1.4 (September 15, 2020): 658–63. http://dx.doi.org/10.30534/ijatcse/2020/9291.42020.
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Xin Liu, L. Ivanescu, Rui Kang, and M. Maier. "Real-time household load priority scheduling algorithm based on prediction of renewable source availability." IEEE Transactions on Consumer Electronics 58, no. 2 (May 2012): 318–26. http://dx.doi.org/10.1109/tce.2012.6227429.
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Hosni, Ines, and Ourida Ben Boubaker. "Optimized scheduling method in 6TSCH wireless networks." International Journal of ADVANCED AND APPLIED SCIENCES 9, no. 10 (October 2022): 81–93. http://dx.doi.org/10.21833/ijaas.2022.10.011.
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IEEE802.15.4e-TSCH is a mode exploited by the Internet of Things. Time Slotted Channel Hopping (TSCH) presents an upgrade to the IEEE 802.15.4 to build a Medium Access Control (MAC) for low power and loss network applications in IoT. This norm defines the concept of TSCH based on channel hopping and reservation of bandwidth to achieve energy efficiency, as well as consistent transmissions. Centralized approaches have been proposed for planning TSCH. They have succeeded in increasing network efficiency and reducing latency, but the scheduling length remains not reduced. However, distributed solutions appear to be more stable in the face of change, without creating a priori assumptions about the topology of the network or the amount of traffic to be transmitted. A distributed scheduling allowing neighboring nodes to decide on a coordination system operated by a minimal scheduling feature is currently proposed by the 6TiSCH working group. This scheduling allows sensor nodes to determine when data is to be sent or received. However, the details of scheduling time intervals are not specified by the TSCH-mode IEEE802.15.4e standard. In this work, we propose a distributed Optimized Minimum Scheduling Function (OMSF) that is based on the 802.15.4e standard TSCH mode. For this purpose, a distributed algorithm is being implemented to predict the scheduling requirements over the next slotframe, focused on the Poisson model and using a cluster tree topology. As a consequence, it will reduce the negotiation operations between the pairs of nodes in each cluster to decide on a schedule. This prediction allowed us to deduce the number of cells needed in the next slotframe. Clustering decreases, the overhead processing costs that produce the prediction model. So, an energy-efficient data collection model focused on clustering and prediction has been proposed. As a result, the energy consumption, traffic load, latency, and queue size in the network, have been reduced.
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Singh, Navneet, Asheesh Singh, and Manoj Tripathy. "Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting." Journal of Electrical Engineering 63, no. 3 (May 1, 2012): 153–61. http://dx.doi.org/10.2478/v10187-012-0023-9.
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Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load ForecastingFor power industries electricity load forecast plays an important role for real-time control, security, optimal unit commitment, economic scheduling, maintenance, energy management, and plant structure planningetc. A new technique for long term load forecasting (LTLF) using optimized feed forward artificial neural network (FFNN) architecture is presented in this paper, which selects optimal number of neurons in the hidden layer as well as the best training method for the case study. The prediction performance of proposed technique is evaluated using mean absolute percentage error (MAPE) of Thailand private electricity consumption and forecasted data. The results obtained are compared with the results of classical auto-regressive (AR) and moving average (MA) methods. It is, in general, observed that the proposed method is prediction wise more accurate.
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Zhang, Gang, Hongchi Liu, Pingli Li, Meng Li, Qiang He, Hailiang Chao, Jiangbin Zhang, and Jinwang Hou. "Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM." Complexity 2020 (January 20, 2020): 1–20. http://dx.doi.org/10.1155/2020/6940786.
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Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.