Enhanced Day-Ahead Electricity Price Forecasting Using a Convolutional Neural Network–Long Short-Term Memory Ensemble Learning Approach with Multimodal Data Integration

Ziyang Wang (), Masahiro Mae, Takeshi Yamane, Masato Ajisaka, Tatsuya Nakata and Ryuji Matsuhashi
Additional contact information
Ziyang Wang: Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan
Masahiro Mae: Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan
Takeshi Yamane: Department of Energy Systems Research and Development, KYOCERA Corporation, Yokohama 220-0012, Japan
Masato Ajisaka: Department of Energy Systems Research and Development, KYOCERA Corporation, Yokohama 220-0012, Japan
Tatsuya Nakata: Department of Energy Systems Research and Development, KYOCERA Corporation, Yokohama 220-0012, Japan
Ryuji Matsuhashi: Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan

Energies, 2024, vol. 17, issue 11, 1-17

Abstract: Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to nearly zero JPY/kWh during peak RES production periods, substantially affecting transactions between electricity retailers and consumers. This paper introduces an innovative DAEPF framework employing a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to predict day-ahead electricity prices in the Kyushu area of Japan. To mitigate the inherent uncertainties associated with neural networks, a novel ensemble learning approach is implemented to bolster the DAEPF model’s robustness and prediction accuracy. The CNN–LSTM model is verified to outperform a standalone LSTM model in both prediction accuracy and computation time. Additionally, applying a natural logarithm transformation to the target day-ahead electricity price as a pre-processing technique has proven necessary for higher prediction accuracy. A novel “policy-versus-policy” strategy is proposed to address the prediction problem of the zero prices, halving the computation time of the traditional two-stage method. The efficacy of incorporating a suite of multimodal features: areal day-ahead electricity price, day-ahead system electricity price, areal actual power generation, areal meteorological forecasts, calendar forecasts, alongside the rolling features of areal day-ahead electricity price, as explanatory variables to significantly enhance DAEPF accuracy has been validated. With the full integration of the proposed features, the CNN–LSTM ensemble model achieves its highest accuracy, reaching performance metrics of R 2 , MAE, and RMSE of 0.787, 1.936 JPY/kWh, and 2.630 JPY/kWh, respectively, during the test range from 1 March 2023 to 31 March 2023, underscoring the advantages of a comprehensive, multi-dimensional approach to DAEPF.

Keywords: day-ahead electricity price forecasting (DAEPF); renewable energy sources (RES); CNN–LSTM; deep learning; ensemble learning; zero day-ahead electricity price (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/11/2687/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/11/2687/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:11:p:2687-:d:1406905

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().