Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting

Arpita Samanta Santra,Jun-Lin Lin

doi:10.3390/en12112040

Abstract

Electricity load forecasting is an important task for enhancing energy efficiency and operation reliability of the power system. Forecasting the hourly electricity load of the next day assists in optimizing the resources and minimizing the energy wastage. The main motivation of this study was to improve the robustness of short-term load forecasting (STLF) by utilizing long short- term memory (LSTM) and genetic algorithm (GA). The proposed method is novel: LSTM networks are designed to avoid the problem of long-term dependencies, and GA is used to obtain the optimal LSTM’s parameters, which are then applied to predict the hourly electricity load for the next day. The proposed method was trained using actual load and weather data, and the performance results showed that it yielded small mean absolute percentage error on the test data.

Highlights

Electricity load forecasting is a mandatory procedure in the capacity planning process of the power industry
Short-term load forecasting (STLF), the focus of this study, predicts hourly or daily electricity load one hour to one week ahead, which is crucial for resource planning and load balancing in power system management [1,2,3]
This study proposed a new method that integrated genetic algorithm (GA) and the long shortterm memory (LSTM) for STLF

Summary

Introduction

Electricity load forecasting is a mandatory procedure in the capacity planning process of the power industry. Short-term load forecasting (STLF), the focus of this study, predicts hourly or daily electricity load one hour to one week ahead, which is crucial for resource planning and load balancing in power system management [1,2,3]. An increase of only 1% in forecast error caused an increase of 10 million pounds in operating cost per year for an electric utility company in the United Kingdom [4,5,6]. Due to the economic and the environmental concerns, electricity load forecasting has drawn considerable attention from both the academic and industrial field

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Conclusion