Predicting Electric Energy Consumption for a Jerky Enterprise

• 作者: Agata Kalganova
• 来源期刊: 能源与动力工程(英文)
• 学科: 工学
• 关键词: Autoregressive Integrated Moving Average Method Artificial Neural Networks Classification and Regression Trees Electricity Consumption Ener-gy Forecasting
• 年，卷（期）: 2020,（6）
• 页码范围: 396-406
• 页数: 11页
• 分类号: TP3

摘要

Wholesale and retail markets for electricity and power require consumers to forecast electricity consumption at different time intervals. The study aims to</span><span style="font-family:Verdana;"> 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 </span><span style="font-family:Verdana;">energy, because power cannot be stockpiled. Therefore, when buying excess electrical power, costs can increase either by selling it on the balancing energy </span><span style="font-family:Verdana;">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 </span><span style="font-family:Verdana;">used to make day, week and month ahead prediction. The prediction effect of</span><span> </span><span style="font-family:Verdana;">prediction 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 </span><span style="font-family:Verdana;">allowed reducing the cost of electricity more efficiently. However, for mid-</span></span><span style="font-family:""> </span><span style="font-family:Verdana;">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.