咨询与建议

看过本文的还看了

相关文献

该作者的其他文献

文献详情 >Predicting Electric Energy Con... 收藏

Predicting Electric Energy Consumption for a Jerky Enterprise

Predicting Electric Energy Consumption for a Jerky Enterprise

作     者:Elena Kapustina Eugene Shutov Anna Barskaya Agata Kalganova Elena Kapustina;Eugene Shutov;Anna Barskaya;Agata Kalganova

作者机构:National Research Tomsk Polytechnic University Tomsk Russia 

出 版 物:《Energy and Power Engineering》 (能源与动力工程(英文))

年 卷 期:2020年第12卷第6期

页      面:396-406页

学科分类:08[工学] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

主  题:Autoregressive Integrated Moving Average Method Artificial Neural Networks Classification and Regression Trees Electricity Consumption Ener-gy Forecasting 

摘      要:Wholesale and retail markets for electricity and power require consumers to forecast electricity consumption at different time intervals. The study aims to increase economic efficiency of the enterprise through the introduction of algorithm for forecasting electric energy consumption unchanged in technological process. Qualitative forecast allows you to essentially reduce costs of electrical energy, because power cannot be stockpiled. Therefore, when buying excess electrical power, costs can increase either by selling it on the balancing energy market or by maintaining reserve capacity. If the purchased power is insufficient, the costs increase is due to the purchase of additional capacity. This paper illustrates three methods of forecasting electric energy consumption: autoregressive integrated moving average method, artificial neural networks and classification and regression trees. Actual data from consuming of electrical energy was used to make day, week and month ahead prediction. The prediction effect ofprediction model was proved in Statistica simulation environment. Analysis of estimation of the economic efficiency of prediction methods demonstrated that the use of the artificial neural networks method for short-term forecast allowed reducing the cost of electricity more efficiently. However, for mid-range predictions, the classification and regression tree was the most efficient method for a Jerky Enterprise. The results indicate that calculation error reduction allows decreases expenses for the purchase of electric energy.

读者评论 与其他读者分享你的观点

用户名:未登录
我的评分