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The improved integrated Exponential Smoothing based CNN-LSTM algorithm to forecast the day ahead electricity price.
Shejul, Kunal; Harikrishnan, R; Gupta, Harshita.
Afiliación
  • Shejul K; Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.
  • Harikrishnan R; Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.
  • Gupta H; Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.
MethodsX ; 13: 102923, 2024 Dec.
Article en En | MEDLINE | ID: mdl-39263362
ABSTRACT
The deregulation of electricity market has led to the development of the short-term electricity market. The power generators and consumers can sell and purchase the electricity in the day ahead terms. The market clearing electricity price varies throughout the day due to the increase in the consumers bidding for electricity. Forecasting of the electricity in the day ahead market is of significance for appropriate bidding. To predict the electricity price the modified method of Exponential Smoothing-CNN-LSTM is proposed based on the time series method of Exponential Smoothing and Deep Learning methods of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The dataset used for assessment of the forecasting algorithms is collected from the day ahead electricity market at the Indian Energy Exchange (IEX). The forecasting results of the Exponential Smoothing-CNN-LSTM method evaluated in terms of Mean Absolute Error (MAE) as 0.11, Root Mean Squared Error (RMSE) as 0.17 and Mean Absolute Percentage Error (MAPE) as 1.53 % indicates improved performance. The proposed algorithm can be used to forecast the time series in other domains as finance, retail, healthcare, manufacturing.•The method of Exponential Smoothing-CNN-LSTM is proposed for forecasting the electricity price a day ahead for accurate bidding for the short-term electricity market participants.•The forecasting results indicate the better performance of the proposed method than the existing techniques of Exponential Smoothing, LSTM and CNN-LSTM due to the advantages of the Exponential Smoothing to extract the levels and seasonality and with the CNN-LSTM methods ability to model the complex spatial and temporal dependencies in the time series.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MethodsX Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
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Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MethodsX Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Países Bajos