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1
Gerossier, Alexis, Robin Girard, and George Kariniotakis. "Modeling and Forecasting Electric Vehicle Consumption Profiles." Energies 12, no. 7 (April 8, 2019): 1341. http://dx.doi.org/10.3390/en12071341.
Повний текст джерелаАнотація:
The growing number of electric vehicles (EV) is challenging the traditional distribution grid with a new set of consumption curves. We employ information from individual meters at charging stations that record the power drawn by an EV at high temporal resolution (i.e., every minute) to analyze and model charging habits. We identify five types of batteries that determine the power an EV draws from the grid and its maximal capacity. In parallel, we identify four main clusters of charging habits. Charging habit models are then used for forecasting at short and long horizons. We start by forecasting day-ahead consumption scenarios for a single EV. By summing scenarios for a fleet of EVs, we obtain probabilistic forecasts of the aggregated load, and observe that our bottom-up approach performs similarly to a machine-learning technique that directly forecasts the aggregated load. Secondly, we assess the expected impact of the additional EVs on the grid by 2030, assuming that future charging habits follow current behavior. Although the overall load logically increases, the shape of the load is marginally modified, showing that the current network seems fairly well-suited to this evolution.
2
Panda, Sujit Kumar, Alok Kumar Jagadev, and Sachi Nandan Mohanty. "Forecasting Methods in Electric Power Sector." International Journal of Energy Optimization and Engineering 7, no. 1 (January 2018): 1–21. http://dx.doi.org/10.4018/ijeoe.2018010101.
Повний текст джерелаАнотація:
Electric power plays a vibrant role in economic growth and development of a region. There is a strong co-relation between the human development index and per capita electricity consumption. Providing adequate energy of desired quality in various forms in a sustainable manner and at a competitive price is one of the biggest challenges. To meet the fast-growing electric power demand, on a sustained basis, meticulous power system planning is required. This planning needs electrical load forecasting as it provides the primary inputs and enables financial analysis. Accurate electric load forecasts are helpful in formulating load management strategies in view of different emerging economic scenarios, which can be dovetailed with the development plan of the region. The objective of this article is to understand various long term electrical load forecasting techniques, to assess its applicability; and usefulness for long term electrical load forecasting for an isolated remote region, under different growth scenarios considering demand side management, price and income effect.
3
Karpenko, Sergey, and Nadezhda Karpenko. "Analysis and modeling of regional electric power consumption subject to influence of external factors." Energy Safety and Energy Economy 3 (June 2021): 12–17. http://dx.doi.org/10.18635/2071-2219-2021-3-12-17.
Повний текст джерелаАнотація:
Electric power consumption along with a large variety of factors affecting it can be forecasted and modelled by using econometric forecasting methods, including time series and correlation and regression analysis. For the purpose of this research, electric power consumption in the Moscow Region, Russia, was modelled with consideration of economic and climate factors based on 2019–2020 power usage data. A multiplicative model for regional electric power consumption and correlations between electric power consumption and an air temperature as well as a total number of cloudy days a month were built. The results will be helpful for analyzing and forecasting of processes involved in power consumption.
4
Parate, Aaditi, and Sachin Bhoite. "Individual Household Electric Power Consumption Forecasting using Machine Learning Algorithms." International Journal of Computer Applications Technology and Research 8, no. 9 (September 17, 2019): 371–76. http://dx.doi.org/10.7753/ijcatr0809.1007.
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5
Klyuev, Roman V., Irbek D. Morgoev, Angelika D. Morgoeva, Oksana A. Gavrina, Nikita V. Martyushev, Egor A. Efremenkov, and Qi Mengxu. "Methods of Forecasting Electric Energy Consumption: A Literature Review." Energies 15, no. 23 (November 25, 2022): 8919. http://dx.doi.org/10.3390/en15238919.
Повний текст джерелаАнотація:
Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on the means and methods of planning electricity production. Forecasting is one of the planning tools since the availability of an accurate forecast is a mechanism for increasing the validity of management decisions. This study provides an overview of the methods used to predict electricity supply requirements to different objects. The methods have been reviewed analytically, taking into account the forecast classification according to the anticipation period. In this way, the methods used in operative, short-term, medium-term, and long-term forecasting have been considered. Both classical and modern forecasting methods have been identified when forecasting electric energy consumption. Classical forecasting methods are based on the theory of regression and statistical analysis (regression, autoregressive models); probabilistic forecasting methods and modern forecasting methods use classical and deep-machine-learning algorithms, rank analysis methodology, fuzzy set theory, singular spectral analysis, wavelet transformations, Gray models, etc. Due to the need to take into account the specifics of each subject area characterizing an energy facility to obtain reliable forecast results, power consumption modeling remains an urgent task despite a wide variety of other methods. The review was conducted with an assessment of the methods according to the following criteria: labor intensity, requirements for the initial data set, scope of application, accuracy of the forecasting method, the possibility of application for other forecasting horizons. The above classification of methods according to the anticipation period allows highlights the fact that when predicting power consumption for different time intervals, the same methods are often used. Therefore, it is worth emphasizing the importance of classifying the forecast over the forecasting horizon not to differentiate the methods used to predict electricity consumption for each period but to consider the specifics of each type of forecasting (operative, short-term, medium-term, long-term).
6
Hoshimov, F. A., I. I. Bakhadirov, A. A. Alimov, and M. T. Erejepov. "Forecasting the electric consumption of objects using artificial neural networks." E3S Web of Conferences 216 (2020): 01170. http://dx.doi.org/10.1051/e3sconf/202021601170.
Повний текст джерелаАнотація:
The possibility of using artificial neural networks of the Matlab mathematical package for predicting the power consumption of objects is considered, the parameters that affect the power consumption are studied.
7
Song, Xinfu, Gang Liang, Changzu Li, and Weiwei Chen. "Electricity Consumption Prediction for Xinjiang Electric Energy Replacement." Mathematical Problems in Engineering 2019 (March 20, 2019): 1–11. http://dx.doi.org/10.1155/2019/3262591.
Повний текст джерелаАнотація:
In recent years, the phenomenon of wind and solar energy abandoned in Xinjiang’s new energy has become severe, the contradiction between the supply and demand of the power grid is obvious, and the proportion of power in the energy consumption structure is relatively low, thus hindering the development of Xinjiang’s green power. In this context, the focus of Xinjiang’s power has shifted to promote the development of electric energy replacement. Therefore, using the Xinjiang region as an example, we first select the important indicators such as the terminal energy substitution in Xinjiang, added value of the secondary industry, population, terminal power consumption intensity, and per capita disposable income. Subsequently, eight combined forecasting models based on the grey model (GM), multiple linear regression (MLR), and error back propagation neural network (BP) are constructed to predict and analyse the electricity consumption of the whole society in Xinjiang. The results indicate the optimal weighted combination forecasting model, GM-MLR-BP of the induced ordered weighted harmonic averaging operator (IOWHA operator), exhibits better prediction accuracy, and the effectiveness of the proposed method is proven.
8
Wu, Tan, De, Pu, Wang, Tan, and Ju. "Multiple Scenarios Forecast of Electric Power Substitution Potential in China: From Perspective of Green and Sustainable Development." Processes 7, no. 9 (September 2, 2019): 584. http://dx.doi.org/10.3390/pr7090584.
Повний текст джерелаАнотація:
To achieve sustainable social development, the Chinese government conducts electric power substitution strategy as a green move. Traditional fuels such as coal and oil could be replaced by electric power to achieve fundamental transformation of energy consumption structure. In order to forecast and analyze the developing potential of electric power substitution, a forecasting model based on a correlation test, the cuckoo search optimization (CSO) algorithm and extreme learning machine (ELM) method is constructed. Besides, China’s present situation of electric power substitution is analyzed as well and important influencing factors are selected and transmitted to the CSO-ELM model to carry out the fitting analysis. The results showed that the CSO-ELM model has great forecasting accuracy. Finally, combining with the cost, policy supports, subsidy mechanism and China’s power consumption data in the past 21 years, four forecasting scenarios are designed and the forecasting results of 2019–2030 are calculated, respectively. Results under multiple scenarios may give suggestions for future sustainable development.
9
Karpenko, S. M., N. V. Karpenko, and G. Y. Bezginov. "Forecasting of power consumption at mining enterprises using statistical methods." Mining Industry Journal (Gornay Promishlennost), no. 1/2022 (March 15, 2022): 82–88. http://dx.doi.org/10.30686/1609-9192-2022-1-82-88.
Повний текст джерелаАнотація:
Forecasting of electric power consumption with due account of assessed impact of various factors helps to make efficient technical and managerial decisions to optimize the electric power consumption processes, including preparation of bids for the wholesale electric power and capacity market. The article uses multivariate methods of statistical analysis and econometric methods based on time series analysis for model designing. The paper presents the results of developing the following models: a multifactor model of electrical power consumption using the regression analysis, the Principal Component Method with the assessment of the impact of production factors on electrical power consumption using elasticity coefficients, as well as the energy saving factor based on a variable structure model; trend additive and multiplicative forecast models of electrical consumption that take into account the seasonality factor, models with a change in trends, a linear dynamic model of electrical power consumption that takes into account the production output; a forecast adaptive polynomial model of electrical power consumption as well as the Winters model. The developed forecast models have a sufficiently high accuracy (accuracy of the MAPE was below 7%). The choice of the model type to forecast the electrical power consumption depends on the quantitative and qualitative characteristics of the time series, the structural relation between the series, the purpose and objectives of the modeling. In order to enhance the accuracy of the forecast it is required to regularly refine the model and adjust it to the actual situation with the due account of new factors and production trends while building different versions of scenarios and combined forecast models of electrical power consumption
10
Deng, Chengbin, Weiying Lin, Xinyue Ye, Zhenlong Li, Ziang Zhang, and Ganggang Xu. "Social media data as a proxy for hourly fine-scale electric power consumption estimation." Environment and Planning A: Economy and Space 50, no. 8 (July 3, 2018): 1553–57. http://dx.doi.org/10.1177/0308518x18786250.
Повний текст джерелаАнотація:
Accurate forecasting of electric demand is essential for the operation of modern power system. Inaccurate load forecasting will considerably affect the power grid efficiency. Forecasting the electric demand for a small area, such as a building, has long been a well-known challenge. In this research, we examined the association between geotagged tweets and hourly electric consumption at a fine scale. All available geotagged tweets and electric meter readings were retrieved and spatially aggregated to each building in the study area. Comparing to traditional studies, the usage of geotagged tweets is to reflect human activity dynamics to some degree by considering human beings as sensors, which therefore can be employed at the building level. High correlation is found between the human activity indicator and the power consumption as supported by a correlation coefficient level over 0.8. To the best of our knowledge, rare studies placed an emphasis on hourly electric power consumption using social media data, especially at such a fine scale. This research shows the great potential of using Twitter data as a proxy of human activities to model hourly electric power consumption at the building level. More studies are warranted in the future to further examine the effectiveness of the proposed method in this research.
11
Khan, Anam-Nawaz, Naeem Iqbal, Atif Rizwan, Rashid Ahmad, and Do-Hyeun Kim. "An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings." Energies 14, no. 11 (May 23, 2021): 3020. http://dx.doi.org/10.3390/en14113020.
Повний текст джерелаАнотація:
Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting.
12
Son, Namrye, Seunghak Yang, and Jeongseung Na. "Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting." Applied Sciences 10, no. 18 (September 17, 2020): 6489. http://dx.doi.org/10.3390/app10186489.
Повний текст джерелаАнотація:
Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict the power demands by analyzing the customers’ power consumption patterns for power supply stabilization. For this, a medium- and long-term power forecasting is proposed. The electricity demand forecast was divided into medium-term and long-term load forecast for customers with different power consumption patterns. Among various deep learning methods, deep neural networks (DNNs) and long short-term memory (LSTM) were employed for the time series prediction. The DNN and LSTM performances were compared to verify the proposed model. The two models were tested, and the results were examined with the accuracies of the six most commonly used evaluation measures in the medium- and long-term electric power load forecasting. The DNN outperformed the LSTM, regardless of the customer’s power pattern.
13
Son, Namrye. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting." Sustainability 13, no. 22 (November 12, 2021): 12493. http://dx.doi.org/10.3390/su132212493.
Повний текст джерелаАнотація:
Electricity demand forecasting enables the stable operation of electric power systems and reduces electric power consumption. Previous studies have predicted electricity demand through a correlation analysis between power consumption and weather data; however, this analysis does not consider the influence of various factors on power consumption, such as industrial activities, economic factors, power horizon, and resident living patterns of buildings. This study proposes an efficient power demand prediction using deep learning techniques for two industrial buildings with different power consumption patterns. The problems are presented by analyzing the correlation between the power consumption and weather data by season for industrial buildings with different power consumption patterns. Four models were analyzed using the most important factors for predicting power consumption and weather data (temperature, humidity, sunlight, solar radiation, total cloud cover, wind speed, wind direction, humidity, and vapor pressure). The prediction horizon for power consumption forecasting was kept at 24 h. The existing deep learning methods (DNN, RNN, CNN, and LSTM) cannot accurately predict power consumption when it increases or decreases rapidly. Hence, a method to reduce this prediction error is proposed. DNN, RNN, and LSTM were superior when using two-year electricity consumption rather than one-year electricity consumption and weather data.
14
Ragu, Vasanth, Seung-Weon Yang, Kangseok Chae, Jangwoo Park, Changsun Shin, Su Young Yang, and Yongyun Cho. "Analysis and Forecasting of Electric Power Energy Consumption in IoT Environments." International Journal of Grid and Distributed Computing 11, no. 6 (June 30, 2018): 1–14. http://dx.doi.org/10.14257/ijgdc.2018.11.6.01.
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15
Peña-Guzmán, Carlos, and Juliana Rey. "Forecasting residential electric power consumption for Bogotá Colombia using regression models." Energy Reports 6 (February 2020): 561–66. http://dx.doi.org/10.1016/j.egyr.2019.09.026.
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16
Kassem, Sameh A., Abdulla H. A. EBRAHIM, Abdulla M. Khasan, and Alla G. Logacheva. "FORECASTING ELECTRIC CONSUMPTION OF THE ENTERPRISE USING ARTIFICIAL NEURAL NETWORKS." Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy 7, no. 1 (2021): 177–93. http://dx.doi.org/10.21684/2411-7978-2021-7-1-177-193.
Повний текст джерелаАнотація:
Energy consumption has increased dramatically over the past century due to many factors, including both technological, social and economic factors. Therefore, predicting energy consumption is of great importance for many parameters, including planning, management, optimization and conservation. Data-driven models for predicting energy consumption have grown significantly over the past several decades due to their improved performance, reliability, and ease of deployment. Artificial neural networks are among the most popular data-driven approaches among the many different types of models today. This article discusses the possibility of using artificial neural networks for medium-term forecasting of the power consumption of an enterprise. The task of constructing an artificial neural network using a feedback algorithm for training a network based on the Matlab mathematical package has been implemented. The authors have analyzed such characteristics as parameter setting, implementation complexity, learning rate, convergence of the result, forecasting accuracy, and stability. The results obtained led to the conclusion that the feedback algorithm is well suited for medium-term forecasting of power consumption.
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Bershadsky, Ilya, Sergey Dzhura, and Aurika Chursinova. "The use of artificial intelligence to predict electric power consumption of a power supply company." Science Bulletin of the Novosibirsk State Technical University, no. 4 (December 18, 2020): 7–16. http://dx.doi.org/10.17212/1814-1196-2020-4-7-16.
Повний текст джерелаАнотація:
The existing approaches to using artificial intelligence in training the neural network using the Neurosimulator 5.0 application to predict electricity consumption according to the data of the previous period are analyzed in this article. It is also concluded that it is advisable to develop this direction of calculations for forecasting and designing power supply systems. The article is devoted to the problem of choosing a model for forecasting electricity consumption when solving the problem of operational daily planning of electricity supplies in the wholesale market. The task of forecasting electricity consumption acquired particular relevance after the emergence of the wholesale electricity market: an underestimation of the forecast leads to the need to launch expensive emergency power plants, while an overestimation leads to an increase in the costs of maintaining excess capacity. The choice of artificial neural networks for this purpose is well-founded. The most suitable architecture of an artificial neural network for solving the problem in question is a multilayer perceptron containing several layers of neurons: an input layer, one or more hidden layers and a layer of output neurons. The transmission of information usually takes place in one direction - from the input layer to the output layer. An example of power consumption prediction based on the results of the nearest measurements in the time domain is considered and an approximation error is determined. The results of approximation and prediction of power consumption showed that a root-mean-square relative error did not exceed 6.32 %, but there is an outlier at one point up to 34 %. The reserve for improving the forecast accuracy is to study the influence of additional factors such as an ambient temperature and the day factor which takes into account the load distribution by the days of the week.
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Shi, Jiarong, and Zhiteng Wang. "A Hybrid Forecast Model for Household Electric Power by Fusing Landmark-Based Spectral Clustering and Deep Learning." Sustainability 14, no. 15 (July 28, 2022): 9255. http://dx.doi.org/10.3390/su14159255.
Повний текст джерелаАнотація:
Household power load forecasting plays an important role in the operation and planning of power grids. To address the prediction issue of household power consumption in power grids, this paper chooses a time series of historical power consumption as the feature variables and uses landmark-based spectral clustering (LSC) and a deep learning model to cluster and predict the power consumption dataset, respectively. Firstly, the investigated data are reshaped into a matrix and all missing entries are recovered by matrix completion. Secondly, the data samples are divided into three clusters by the LSC method according to the periodicity and regularity of power consumption. Then, all samples in each cluster are expanded via bootstrap aggregating technique. Subsequently, a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) is employed to predict power consumption. The goal of CNN is to extract the features from input data in sequence learning, and LSTM aims to train and predict the power consumption. Finally, the forecasting performance of the LSC–CNN–LSTM is compared with several other deep learning models to verify its reliability and effectiveness in the field of household power load. The experimental results show that the proposed hybrid method is superior to other state-of-the-art deep learning techniques in forecasting performance.
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Tay, K. G., Y. Y. Choy, and C. C. Chew. "Forecasting Electricity Consumption Using Fuzzy Time Series." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 342. http://dx.doi.org/10.14419/ijet.v7i4.30.22305.
Повний текст джерелаАнотація:
Electricity consumption forecasting is important for effective operation, planning and facility expansion of power system. Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development. There is a great development of Universiti Tun Hussein Onn Malaysia (UTHM) infrastructure since its formation in 1993. The development will be accompanied with the increasing demand of electricity. Hence, there is a need to forecast the UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. Therefore, in this study, the Fuzzy time series (FTS) with trapezoidal membership function was implemented on the UTHM monthly electricity consumption from January 2011 to December 2017 to forecast January to December 2018 monthly electricity consumption. The procedure of the FTS and trapezoidal membership function was described together with January data. FTS is able to forecast UTHM electricity consumption quite well.
20
Yan, Ke, Xudong Wang, Yang Du, Ning Jin, Haichao Huang, and Hangxia Zhou. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy." Energies 11, no. 11 (November 8, 2018): 3089. http://dx.doi.org/10.3390/en11113089.
Повний текст джерелаАнотація:
Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage.
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Meng, Ming, Wei Shang, and Dongxiao Niu. "Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/243171.
Повний текст джерелаАнотація:
Monthly electric energy consumption forecasting is important for electricity production planning and electric power engineering decision making. Multiwindow moving average algorithm is proposed to decompose the monthly electric energy consumption time series into several periodic waves and a long-term approximately exponential increasing trend. Radial basis function (RBF) artificial neural network (ANN) models are used to forecast the extracted periodic waves. A novel hybrid growth model, which includes a constant term, a linear term, and an exponential term, is proposed to forecast the extracted increasing trend. The forecasting results of the monthly electric energy consumption can be obtained by adding the forecasting values of each model. To test the performance by comparison, the proposed and other three models are used to forecast China's monthly electric energy consumption from January 2011 to December 2012. Results show that the proposed model exhibited the best performance in terms of mean absolute percentage error (MAPE) and maximal absolute percentage error (MaxAPE).
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Ahmed, Nawzad M., and Ayad O. Hamdeen. "Predicting Electric Power Energy, Using Recurrent Neural Network Forecasting Model." Journal of University of Human Development 4, no. 2 (June 30, 2018): 53. http://dx.doi.org/10.21928/juhd.v4n2y2018.pp53-60.
Повний текст джерелаАнотація:
Electricity is counted as a one of the most important energy sources in the world. It has played a main role in developing several sectors. In this study two types of electricity variables have been used, the first was the demand on power energy, and the second was the consumption or energy load in Sulaimani city. The main goal of the study was to construct an analytic model of the recurrent neural network (RNN) for both variables. This model has a great ability in detecting the complex patterns for the data of a time series, which is most suitable for the data under consideration. This model is also more sensitive and reliable than the other artificial neutral network (ANN), so it can deal with more complex data that might be chaotic, seismic….etc. this model can also deal with nonlinear data which are mostly found in time series, and it deals with them differently compared to the other models. This research determined and defined the best model of RNN for electricity demand and consumption to be run in two levels. The first level is to predict the complexity of the suggested model (1-5-10-1) with the error function as (MSE: mean square error, AIC, and R2: coefficient of determination). The second level uses the suggested model to forecast the demand on electric power energy and the value of each unit. Another result of this study is to determine the suitable algorithm that can deal with such complex data. The algorithm (Levenberg-Marquardt) was found to be the most reliable and has the most optimal time to give accurate and reliable results in this study.
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Liu, Chang, Yuanliang Zhang, Weisong Chen, Haitong Gu, Hui Li, and Shaoliang Chen. "A Short Term Forecasting Method for Regional Power Consumption Considering Related Factors." Journal of Physics: Conference Series 2195, no. 1 (February 1, 2022): 012022. http://dx.doi.org/10.1088/1742-6596/2195/1/012022.
Повний текст джерелаАнотація:
Abstract Analysis and prediction of power consumption law is the basis of power grid planning and construction, and is also an effective guide for energy demand side management. With the rapid development of economy and the complex change of industrial structure in recent years, the internal structure of power demand is changing to some extent. Therefore, a short-term forecasting method of regional electricity consumption considering the related factors is proposed. Based on the analysis results, a short-term prediction model of regional electricity consumption considering the related factors is established, and the short-term prediction is realized by the calculation of the model. Through the example analysis, it is verified that the forecasting deviation of the short-term forecasting method is low and meets the basic requirements of electric quantity forecasting.
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Kalinchyk, Vasyl, Vitaliy Pobigaylo, Vitaliy Kalinchyk, Aleksandr Meita, and Olena Borychenko. "Combined models of electricity consumption." Bulletin of NTU "KhPI". Series: Problems of Electrical Machines and Apparatus Perfection. The Theory and Practice, no. 1 (7) (June 30, 2022): 34–37. http://dx.doi.org/10.20998/2079-3944.2022.1.07.
Повний текст джерелаАнотація:
The article investigates models and methods of electric load forecasting. It is shown that among the known methods of power consumption management, preference is given to those based on the use of forecast estimates. The analysis of works devoted to the issues of forecasting the processes of power consumption management systems of industrial enterprises is carried out. It is shown that it is expedient to use adaptive models as a basis for operative forecasting of loads of power supply systems of industrial enterprises. Analysis of adaptive models of electricity consumption forecasting based on the method of exponential smoothing showed their high efficiency and good adaptability to changes in the process of electricity consumption. It is shown that the greatest difficulty in forecasting are cases of abrupt changes in the development of the process. Abrupt changes in the process can lead to a violation of pre-existing qualitative relationships of the parameters of the projected system. If the jump is the transition of the predicted system from one steady state to another, the model of exponential smoothing with correction of the constant smoothing has the best adaptability to this kind of change. At the same time, changes of the "pulse" type are worked out by the model with a certain delay, which leads to an increase in the standard error of the forecast. Therefore, the model's response to change slows down. To eliminate this circumstance, a forecasting procedure based on combined models is proposed. The paper considers two models of combined forecasting - a combined model of joint processing of forecasting results and a combined model of selective type. Experimental studies of the considered models are carried out.
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Kim, Ji-Yoon, Jong-Hak Lee, Ji-Hyun Oh, and Jin-Seok Oh. "A Comparative Study on Energy Consumption Forecast Methods for Electric Propulsion Ship." Journal of Marine Science and Engineering 10, no. 1 (December 30, 2021): 32. http://dx.doi.org/10.3390/jmse10010032.
Повний текст джерелаАнотація:
Efficient vessel operation may reduce operational costs and increase profitability. This is in line with the direction pursued by many marine industry stakeholders such as vessel operators, regulatory authorities, and policymakers. It is also financially justifiable, as fuel oil consumption (FOC) maintenance costs are reduced by forecasting the energy consumption of electric propulsion vessels. Although recent technological advances demand technology for electric propulsion vessel electric power load forecasting, related studies are scarce. Moreover, previous studies that forecasted the loads excluded various factors related to electric propulsion vessels and failed to reflect the high variability of loads. Therefore, this study aims to examine the efficiency of various multialgorithms regarding methods of forecasting electric propulsion vessel energy consumption from various data sampling frequencies. For this purpose, there are numerous machine learning algorithm sets based on convolutional neural network (CNN) and long short-term memory (LSTM) combination methods. The methodology developed in this study is expected to be utilized in training the optimal energy consumption forecasting model, which will support tracking of degraded performance in vessels, optimize transportation, reflect emissions accurately, and be applied ultimately as a basis for route optimization purposes.
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Filipova-Petrakieva, S. K., and V. Dochev. "Short-Term Forecasting of Hourly Electricity Power Demand." Engineering, Technology & Applied Science Research 12, no. 2 (April 9, 2022): 8374–81. http://dx.doi.org/10.48084/etasr.4787.
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The optimal use of electric power consumption is a fundamental indicator of the normal use of energy resources. Its quantity depends on the loads connected to the electric power grid, which are measured on an hourly basis. This paper examines forecasting methods for hourly electrical power demands for 7 days. Data for the period of 1 January 2015 and 24 December 2020 were processed, while the models' forecasts were tested on actual power load data between 25 and 31 December 2020, obtained from the Energy System Operator of the Republic of Bulgaria. Two groups of methods were used for the prognosis: classical regression methods and clustering algorithms. The first group included "moving window" and ARIMA, while the second examined K-Means, Time Series K-Means, Mini Batch K-Means, Agglomerative clustering, and OPTICS. The results showed high accuracy of the forecasts for the prognosis period.
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Nasution, Aminulsyah, Badriana Badriana, and Andik Bintoro. "Application of The Combined Method in Inventory Forecasting Electricity at PT PLN (Persero) ULP Sibuhuan." International Journal of Engineering, Science and Information Technology 2, no. 4 (December 19, 2022): 111–18. http://dx.doi.org/10.52088/ijesty.v2i4.348.
Повний текст джерелаАнотація:
The forecast of electricity consumption is a prediction of the use of electricity for the future by referring to the use of electricity in past data. Estimates of electricity consumption needs are intended to estimate how much electricity consumption is used by customers and must be provided by PT PLN (PERSERO) ULP SIBUHUAN as a provider of electrical energy services. Population growth occurs along with the development of an area, so it affects the demand for electricity and the need for electricity consumption. Load mapping must be done to maintain the continuity and distribution of electrical energy to customers. One way to preserve the continuity of service is to estimate electric power consumption using several forecasting methods. One of the methods that can be used is to combine several load forecasting methods called the combined method. The combined method is a model that incorporates various estimation methods, including econometrics, analytics, and trends, using historical customer data originating from BPS (Central Statistics Agency) Padang Lawas Regency and PT. PLN (PERSERO) ULP SIBUHUAN. The estimated load between 2021 and 2025 is 625,070,452 kWh, with an increase in electricity consumption of 20.7%, household expenses increasing by 1.8%, and business sector expenses by 1.35%. The method used is to calculate manually using Microsoft Excel to obtain forecasting values for population growth; increasing electricity consumption is very important for planning electric power generation, development, and planning of electric power distribution and mapping of loads on customers.
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LING, S. H., F. H. F. LEUNG, L. K. WONG, and H. K. LAM. "COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR HOME ELECTRIC LOAD FORECASTING AND BALANCING." International Journal of Computational Intelligence and Applications 05, no. 03 (September 2005): 371–91. http://dx.doi.org/10.1142/s1469026805001659.
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The paper presents an electric load balancing system for domestic use. An electric load forecasting system, which is realized by a genetic algorithm-based modified neural network, is employed. On forecasting the home power consumption profile, the load balancing system can adjust the amount of energy stored in battery accordingly, preventing it from reaching certain practical limits. A steady consumption from the AC mains can then be obtained which will benefit both the users and the utility company. An example will be given to illustrate the merits of the forecaster, and its performance on achieving the load balancing.
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Al-Shehri, Abdallah. "A simple forecasting model for industrial electric energy consumption." International Journal of Energy Research 24, no. 8 (2000): 719–26. http://dx.doi.org/10.1002/1099-114x(20000625)24:8<719::aid-er627>3.0.co;2-4.
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Yang, Yi, Zhihao Shang, Yao Chen, and Yanhua Chen. "Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting." Energies 13, no. 3 (January 21, 2020): 532. http://dx.doi.org/10.3390/en13030532.
Повний текст джерелаАнотація:
As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability.
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Wang, Xue. "Power system short-term load forecasting based on BP neural network." Journal of Physics: Conference Series 2378, no. 1 (December 1, 2022): 012068. http://dx.doi.org/10.1088/1742-6596/2378/1/012068.
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Abstract Electric energy plays an important role in the development of national industries. Based on the characteristics of China’s electricity load, this paper uses the signal decomposition of BP neural network, deep learning neural network and intelligent optimization basis to conduct research on short-term electricity load forecasting of Chinese national electricity consumption.
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Efremenko, Vladimir, Roman Belyaevsky, and Evgeniya Skrebneva. "The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption." E3S Web of Conferences 21 (2017): 02002. http://dx.doi.org/10.1051/e3sconf/20172102002.
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Lyakhomsky, Alexander, and Andrei Shadrin. "POWER CONSUMPTION FORECASTING BASED ON FULLY CONNECTED FEED-FORWARD NEURAL NETWORKS." Electrical and data processing facilities and systems 18, no. 1 (2022): 107–13. http://dx.doi.org/10.17122/1999-5458-2022-18-1-107-113.
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The relevance The relevance of electricity consumption forecasting on the basis of fully connected feed-forward neural networks (FNN) to improve the validity of applications for electricity is considered. Aim of research Synthesis of predictive model of electricity consumption in the form of four-layer fully connected feed-forward neural network, linking the volume of production and the predicted electricity consumption is performed. Research methods The algorithm of the predictive model development includes: formation and initial statistical processing of initial data; determination of FNN structure hyperparameters — total number of layers, number of neurons in layers, activation function, training rate coefficient; selection of optimization method; training, checking model adequacy. Results The analytical expression for the description of the forecast model based on FNN is given. The synthesized forecast model makes it possible to increase the validity of electric power applications of enterprises.
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Krutsyak, Mykhailo. "FORECASTING DEMAND ON THE DOMESTIC ELECTRICITY MARKET ON THE BASIS OF THE RESULTS OF SOCIAL AND ECONOMIC INDICATORS DYNAMICS ANALYSIS." Economic Analysis, no. 28(3) (2018): 37–46. http://dx.doi.org/10.35774/econa2018.03.037.
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The works, which are devoted to the forecasting of demand for electric power, are analysed in this research. A number of these works is identified in order to use the available data. The influence of individual social and economic factors on the volume of annual electricity consumption in Ukraine is investigated. The use of forecasting of demand for electric energy data on the volume of gross domestic product on the parity of purchasing power, GDP energy intensity and the population of Ukraine for the period of 1991-2017 are substantiated, as well as the correlation between them. The annual volumes of electricity consumption are determined. It has been proposed the economic and mathematical model of forecasting and use of multiple regression equations. The method of reduction of the nonlinearity of the dynamics of the investigated factors is considered. We have compared the results, which are obtained after the use of this model, with the results of the available national forecasts.
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Losev, Denis. "The long-term forecasting of specific fuel consumption by the power system of Uzbekistan." E3S Web of Conferences 216 (2020): 01101. http://dx.doi.org/10.1051/e3sconf/202021601101.
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The article considers the possibility of using the least squares method (LSM) for long-term forecasting of the parameters of the regime of electric power systems. There is presented least squares method for predicting the parameters of the regime of electric power systems. It is shown that, based on the least-squares method, it is possible to obtain prognostic equations, as well as coefficients of approximating functions necessary for the formation of these equations. The results of the analysis of the comparison of linear, hyperbolic, logarithmic, exponential and quadratic functions on the use of LSMs to predict specific fuel consumption are presented. The criterion of the least squares method, which is according for using the statistical data of the control sample in the obtained prognostic functions, the standard deviations are found.
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Ragu, Vasanth, and Younghyun Kim. "A Best Fit Model for Forecasting Korea Electric Power Energy Consumption in IoT Environments." International Journal of Internet of Things and its Applications 2, no. 1 (August 30, 2018): 7–12. http://dx.doi.org/10.21742/ijiota.2018.2.1.02.
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Tang, Junci, Guanfu Wang, Zhiyuan Cai, Xiaodong Zhao, Haoyu Li, Jia Cui, and Zihan Li. "Ultra short term load forecasting for different types of industrial parks with intelligent buildings." Journal of Physics: Conference Series 2378, no. 1 (December 1, 2022): 012082. http://dx.doi.org/10.1088/1742-6596/2378/1/012082.
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Abstract For industrial parks with intelligent buildings, accurate forecasting of various load sizes may reduce the power supply pressure of the power grid. For industrial parks with intelligent buildings, considering the influence of weather factors and the dynamic electricity price game mechanism, the load forecasting of industrial parks often ignores the load of intelligent buildings and electric vehicles, resulting in insufficient satisfaction of residents in the buildings. The improved Attention-LSTM algorithm based on DBN structure is proposed. It takes into account the correlation between loads and the correlation between loads and energy sources. When forecasting high energy consumption industrial loads, the forecasting accuracy of intelligent building loads and electric vehicle loads is improved compared with the original algorithm, which ensures the satisfaction of residents in the building. Finally, an example is given to verify the advantages of the algorithm.
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Kim, Yunsun, and Sahm Kim. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models." Energies 14, no. 5 (March 9, 2021): 1487. http://dx.doi.org/10.3390/en14051487.
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This study compared the methods used to forecast increases in power consumption caused by the rising popularity of electric vehicles (EVs). An excellent model for each region was proposed using multiple scaled geographical datasets over two years. EV charging volumes are influenced by various factors, including the condition of a vehicle, the battery’s state-of-charge (SOC), and the distance to the destination. However, power suppliers cannot easily access this information due to privacy issues. Despite a lack of individual information, this study compared various modeling techniques, including trigonometric exponential smoothing state space (i.e., Trigonometric, Box–Cox, Auto-Regressive-Moving-Average (ARMA), Trend, and Seasonality (TBATS)), autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), and long short-term memory (LSTM) modeling, based on past values and exogenous variables. The effect of exogenous variables was evaluated in macro- and micro-scale geographical areas, and the importance of historic data was verified. The basic statistics regarding the number of charging stations and the volume of charging in each region are expected to aid the formulation of a method that can be used by power suppliers.
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Parejo, Antonio, Stefano Bracco, Enrique Personal, Diego Francisco Larios, Federico Delfino, and Carlos León. "Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models." Applied Sciences 11, no. 14 (July 12, 2021): 6420. http://dx.doi.org/10.3390/app11146420.
Повний текст джерелаАнотація:
Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption–generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy.
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Pavlicko, Michal, Mária Vojteková, and Oľga Blažeková. "Forecasting of Electrical Energy Consumption in Slovakia." Mathematics 10, no. 4 (February 12, 2022): 577. http://dx.doi.org/10.3390/math10040577.
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Prediction of electricity energy consumption plays a crucial role in the electric power industry. Accurate forecasting is essential for electricity supply policies. A characteristic feature of electrical energy is the need to ensure a constant balance between consumption and electricity production, whereas electricity cannot be stored in significant quantities, nor is it easy to transport. Electricity consumption generally has a stochastic behavior that makes it hard to predict. The main goal of this study is to propose the forecasting models to predict the maximum hourly electricity consumption per day that is more accurate than the official load prediction of the Slovak Distribution Company. Different models are proposed and compared. The first model group is based on the transverse set of Grey models and Nonlinear Grey Bernoulli models and the second approach is based on a multi-layer feed-forward back-propagation network. Moreover, a new potential hybrid model combining these different approaches is used to forecast the maximum hourly electricity consumption per day. Various performance metrics are adopted to evaluate the performance and effectiveness of models. All the proposed models achieved more accurate predictions than the official load prediction, while the hybrid model offered the best results according to performance metrics and supported the legitimacy of this research.
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Khan, Noman, Ijaz Ul Haq, Fath U. Min Ullah, Samee Ullah Khan, and Mi Young Lee. "CL-Net: ConvLSTM-Based Hybrid Architecture for Batteries’ State of Health and Power Consumption Forecasting." Mathematics 9, no. 24 (December 20, 2021): 3326. http://dx.doi.org/10.3390/math9243326.
Повний текст джерелаАнотація:
Traditional power generating technologies rely on fossil fuels, which contribute to worldwide environmental issues such as global warming and climate change. As a result, renewable energy sources (RESs) are used for power generation where battery energy storage systems (BESSs) are widely used to store electrical energy for backup, match power consumption and generation during peak hours, and promote energy efficiency in a pollution-free environment. Accurate battery state of health (SOH) prediction is critical because it plays a key role in ensuring battery safety, lowering maintenance costs, and reducing BESS inconsistencies. The precise power consumption forecasting is critical for preventing power shortage and oversupply, and the complicated physicochemical features of batteries dilapidation cannot be directly acquired. Therefore, in this paper, a novel hybrid architecture called ‘CL-Net’ based on convolutional long short-term memory (ConvLSTM) and long short-term memory (LSTM) is proposed for multi-step SOH and power consumption forecasting. First, battery SOH and power consumption-related raw data are collected and passed through a preprocessing step for data cleansing. Second, the processed data are fed into ConvLSTM layers, which extract spatiotemporal features and form their encoded maps. Third, LSTM layers are used to decode the encoded features and pass them to fully connected layers for final multi-step forecasting. Finally, a comprehensive ablation study is conducted on several combinations of sequential learning models using three different time series datasets, i.e., national aeronautics and space administration (NASA) battery, individual household electric power consumption (IHEPC), and domestic energy management system (DEMS). The proposed CL-Net architecture reduces root mean squared error (RMSE) up to 0.13 and 0.0052 on the NASA battery and IHEPC datasets, respectively, compared to the state-of-the-arts. These experimental results show that the proposed architecture can provide robust and accurate SOH and power consumption forecasting compared to the state-of-the-art.
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Bogdanov, R. M., and S. V. Lukin. "Software including the functions of automated analysis of electric power consumption in pipeline oil transportation." Proceedings of the Mavlyutov Institute of Mechanics 8, no. 1 (2011): 233–38. http://dx.doi.org/10.21662/uim2011.1.022.
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Oil and petroleum products transportation is characterized by a significant cost of electric power. Correct oil and petroleum products accounting and forecasting requires knowledge of many factors. The software for norms of electric power consumption analysis for the planned period was developed at the Ufa Scientific Center of the Russian Academy of Sciences. Based on the principles of the relational data model, a schematic diagram/arrangement for the main oil transportation objects was developed, which allows to hold the initial data and calculated parameters in a structured manner.
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Mohammad, Faisal, Mohamed A. Ahmed, and Young-Chon Kim. "Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System." Energies 14, no. 19 (September 27, 2021): 6161. http://dx.doi.org/10.3390/en14196161.
Повний текст джерелаАнотація:
An efficient energy management system is integrated with the power grid to collect information about the energy consumption and provide the appropriate control to optimize the supply–demand pattern. Therefore, there is a need for intelligent decisions for the generation and distribution of energy, which is only possible by making the correct future predictions. In the energy market, future knowledge of the energy consumption pattern helps the end-user to decide when to buy or sell the energy to reduce the energy cost and decrease the peak consumption. The Internet of things (IoT) and energy data analytic techniques have provided the convenience to collect the data from the end devices on a large scale and to manipulate all the recorded data. Forecasting an electric load is fairly challenging due to the high uncertainty and dynamic nature involved due to spatiotemporal pattern consumption. Existing conventional forecasting models lack the ability to deal with the spatio-temporally varying data. To overcome the above-mentioned challenges, this work proposes an encoder–decoder model based on convolutional long short-term memory networks (ConvLSTM) for energy load forecasting. The proposed architecture uses encode consisting of multiple ConvLSTM layers to extract the salient features in the data and to learn the sequential dependency and then passes the output to the decoder, having LSTM layers to make forecasting. The forecasting results produced by the proposed approach are favorably comparable to the existing state-of-the-art and better than the conventional methods with the least error rate. Quantitative analyses show that a mean absolute percentage error (MAPE) of 6.966% for household energy consumption and 16.81% for city-wide energy consumption is obtained for the proposed forecasting model in comparison with existing encoder–decoder-based deep learning models for two real-world datasets.
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Zhang, Suqi, Ningjing Zhang, Ziqi Zhang, and Ying Chen. "Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved Seagull Optimization Algorithm." Energies 15, no. 23 (December 4, 2022): 9197. http://dx.doi.org/10.3390/en15239197.
Повний текст джерелаАнотація:
Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution management system. This study aims to propose a high precision load forecasting method. The power load forecasting model, based on the Improved Seagull Optimization Algorithm, which optimizes SVM (ISOA-SVM), is constructed. First, aiming at the problem that the random selection of internal parameters of SVM will affect its performance, the Improved Seagull Optimization Algorithm (ISOA) is used to optimize its parameters. Second, to solve the slow convergence speed of the Seagull Optimization Algorithm (SOA), three strategies are proposed to improve the optimization performance and convergence accuracy of SOA, and an ISOA algorithm with better optimization performance and higher convergence accuracy is proposed. Finally, the load forecasting model based on ISOA-SVM is established by using the Mean Square Error (MSE) as the objective function. Through the example analysis, the prediction performance of the ISOA-SVM is better than the comparison models and has good prediction accuracy and effectiveness. The more accurate load forecasting can provide guidance for power generation and power consumption planning of the power system.
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Klyuev, Roman V., Igor I. Bosikov, Oksana A. Gavrina, and Vladimir Ch Revazov. "System analysis of power consumption by nonferrous metallurgy enterprises on the basis of rank modeling of individual technocenosis castes." MATEC Web of Conferences 226 (2018): 04018. http://dx.doi.org/10.1051/matecconf/201822604018.
Повний текст джерелаАнотація:
To increase energy efficiency at non-ferrous metallurgy enterprises, an integrated system approach for estimation of electricity consumption is needed. The paper presents the results of a rank analysis of the power consumption of individual castes of process equipment on the basis of an integrated energy survey of the enterprise. A methodology for constructing mathematical models for calculating and predicting electric power consumption for all castes of the ranked H-distribution of technocenosis has been developed. For the first time, according to the established regularity of the H-distribution, a mathematical model for predicting power consumption has been developed, including a quantitative analysis of the energy characteristics of consumers by individual castes of technocenosis. A retrospective check of the relative error in the prediction of electricity consumption showed that for the model it does not exceed 2%, which is significantly lower than the relative error of the prediction for a number of models of other types. The received model is recommended for use in the automated system of dispatching control of power consumption for the purposes of short-term forecasting of electric power consumption at industrial enterprises of non-ferrous metallurgy.
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Khan, Prince Waqas, Yung-Cheol Byun, Sang-Joon Lee, Dong-Ho Kang, Jin-Young Kang, and Hae-Su Park. "Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources." Energies 13, no. 18 (September 17, 2020): 4870. http://dx.doi.org/10.3390/en13184870.
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In today’s world, renewable energy sources are increasingly integrated with nonrenewable energy sources into electric grids and pose new challenges because of their intermittent and variable nature. Energy prediction using soft-computing techniques plays a vital role in addressing these challenges. As electricity consumption is closely linked to other energy sources such as natural gas and oil, forecasting electricity consumption is essential for making national energy policies. In this paper, we utilize various data mining techniques, including preprocessing historical load data and the load time series’s characteristics. We analyzed the power consumption trends from renewable energy sources and nonrenewable energy sources and combined them. A novel machine learning-based hybrid approach, combining multilayer perceptron (MLP), support vector regression (SVR), and CatBoost, is proposed in this paper for power forecasting. A thorough comparison is made, taking into account the results obtained using other prediction methods.
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Zhan, Liping, and Yan Gu. "Research on Multi-scenario Intelligent Forecasting Model of China’s Electric Power Consumption Driven by Policy." IOP Conference Series: Earth and Environmental Science 332 (November 5, 2019): 042020. http://dx.doi.org/10.1088/1755-1315/332/4/042020.
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Gomez-Quiles, Catalina, Gualberto Asencio-Cortes, Adolfo Gastalver-Rubio, Francisco Martinez-Alvarez, Alicia Troncoso, Joan Manresa, Jose C. Riquelme, and Jesus M. Riquelme-Santos. "A Novel Ensemble Method for Electric Vehicle Power Consumption Forecasting: Application to the Spanish System." IEEE Access 7 (2019): 120840–56. http://dx.doi.org/10.1109/access.2019.2936478.
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Zhang, Yang, Zhenghui Fu, Yulei Xie, Qing Hu, Zheng Li, and Huaicheng Guo. "A Comprehensive Forecasting–Optimization Analysis Framework for Environmental-Oriented Power System Management—A Case Study of Harbin City, China." Sustainability 12, no. 10 (May 22, 2020): 4272. http://dx.doi.org/10.3390/su12104272.
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In this study, a comprehensive research framework coupled with electric power demand forecasting, a regional electric system planning model, and post-optimization analysis is proposed for electric power system management. For dealing with multiple forms of uncertainties and dynamics concerning energy utilization, capacity expansions, and environmental protection, the inexact two-stage stochastic robust programming optimization model was developed. The novel programming method, which integrates interval parameter programming (IPP), stochastic robust optimization (SRO), and two-stage stochastic programming (TSP), was applied to electric power system planning and management in Harbin, China. Furthermore, the Gray-Markov approach was employed for effective electricity consumption prediction, and the forecasted results can be described as interval values with corresponding occurrence probability, aiming to produce viable input parameters of the optimization model. Ten scenarios were analyzed with different emissions reduction levels and electricity power structure adjustment modes, and the technique for order of preference by similarity to ideal solution (TOPSIS) was selected to identify the most influential factors of planning decisions by selecting the optimal scheme. The results indicate that a diversified power structure that dominates by thermal power and is mainly supplemented by biomass power should be formed to ensure regional sustainable development and electricity power supply security in Harbin. In addition, power structure adjustment is more effective than the pollutants emission control for electricity power system management. The results are insightful for supporting supply-side energy reform, generating an electricity generation scheme, adjusting energy structures, and formulating energy consumption of local policies.
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Zhao, Lan Guang, Jing Hou, Jin Xiang Pian, and Feng Zhong Zhang. "Electric Energy Demand Forcasting with GRNN for Energy Saving Strategy." Applied Mechanics and Materials 198-199 (September 2012): 639–43. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.639.
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In order to reasonable distribute electric energy and develop an energy-saving plan, electric energy demand forecasting which can represent a fundamental information is necessary. Power consumption has long duration, obvious nonlinear effect and the energy demand may be seen as a temporal series when its data are conveniently arranged. Neural networks have proved to be a very powerful tool which can predict a future value by studying the past one. In this paper, we propose a time series prediction model based on Generalized Regression Neural Network (GRNN) to predict the evolution of the monthly demand of electric consumption. GRNN is trained in just one presentation of the training patterns and is capable of providing fast and accurate results. The performance of the algorithm is evaluated using mean absolute and mean square errors. Tests are carried out by using the power consumption data of a campus distribution subsystem and the results obtained are found to be compatible with the actually data.