Forecasting electricity consumption by LSTM neural network

Abstract

Relevance. The need to enhance the precision of electricity consumption forecasting for improving energy efficiency and, consequently, enhancing the competitiveness of manufactured products by reducing the proportion of electricity costs in their total cost. When determining forecast indicators of electricity consumption by industrial enterprises, it is important to apply contemporary high-precision forecasting methods. Only 20–30 forecasting methods of the 150 existing ones are actively implemented in practice. An examination of prevailing forecasting methodologies used by industrial enterprises reveals that they are mainly based either on expert assessments of electricity volumes or on accounting for specific electricity consumption (per unit of product manufactured). Aim. To elevate the accuracy of electricity consumption forecasting at industrial enterprises by using artificial intelligence methods, specifically, artificial neural network techniques, including the Long-Short Term Memory (LSTM) approach. Methods. When developing the forecasting model, artificial neural network techniques were adopted, with a particular emphasis on the Long-Short Term Memory (LSTM) method. For primary data processing, Gaussian distribution principles and normalization/scaling techniques were applied. Results. Substantiated computationally by applying the proposed model based on the artificial neural network technique for forecasting electricity consumption of industrial enterprises. A significant advantage of this method is its capability for learning and adaptability to forecasting. Real-time computations demonstrate its successful implementation, attributed primarily to appropriate selection of input layers and mitigation of random variables.

Keywords

• forecasting
• power consumption
• forecasting error
• model adequacy
• single-layer neural network
• activation function