Forecasting the Market Clearing Price in Iran’s Electricity Market Using Two Deep Learning-Based Hybrid Models

Document Type : Research Paper

Authors

1 Master’s Student in Energy Economics, Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran

2 Associate Professor, Department of Energy, Agricultural and Environmental Economics, Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran

3 Department of Energy, Agricultural and Environmental Economics, Faculty of Economics, Allameh Tabataba’I University, Tehran, Iran

Abstract

Electricity is a unique commodity, and forecasting its price is challenging due to its distinct characteristics. Accurate electricity price forecasting is essential for market participants, as it can help reduce risk, increase economic profitability, and enhance power system stability. In the electricity price forecasting literature, machine learning models have been preferred over other models due to their ability to capture the nonlinear behavior of market data, ease of implementation, and good performance. In recent years, the emphasis on the importance of the number of hidden layers in machine learning structures has led to the emergence of deep learning. However, the performance of these models is significantly influenced by optimal feature selection and appropriate hyperparameter tuning. Therefore, this study aims to forecast the market clearing price in Iran’s electricity market by employing two deep learning-based hybrid models: deep neural networks (DNN) and long short-term memory (LSTM) networks, using feature selection and hyperparameter optimization techniques. The forecasting accuracy of the models is then compared. The results indicate that the LSTM-based hybrid model outperforms the DNN-based model.

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Main Subjects

References

منابع
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Iranian Economic Development Analyses
Volume 10, Issue 2
October 2024

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