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

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  • Arpita Samanta Santra

    (Department of Information Management, Yuan Ze University, Taoyuan 32003, Taiwan)

  • Jun-Lin Lin

    (Department of Information Management, Yuan Ze University, Taoyuan 32003, Taiwan
    Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 32003, Taiwan)

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.

Suggested Citation

  • Arpita Samanta Santra & Jun-Lin Lin, 2019. "Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 12(11), pages 1-11, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2040-:d:234951
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    References listed on IDEAS

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