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<p>The increasing volatility of electricity prices, driven by the growing share of renewable energy, calls for new approaches. This paper proposes a dynamic Bayesian network (DBN) method for electricity price interval forecasting. The model uses predicted values of wind power generation, total power generation, and total electricity consumption, along with historical electricity prices, as inputs. The network structure is determined using a greedy search algorithm, and the model parameters are estimated through maximum likelihood estimation (MLE). By treating the predictions of wind power, total generation, and total consumption as reasoning evidence, the method employs joint tree inference to generate discrete states and posterior probabilities for electricity prices, thereby enabling interval forecasting. The DBN-based interval predictions achieve a prediction interval coverage probability (PICP) of 95.24%, a normalized average width (PINAW) of 9.25%, and an accumulated width deviation (AWD) of 0.56%. The effectiveness of the proposed method was evaluated by comparing its predictions with actual electricity prices and with results from both particle swarm optimization-kernel extreme learning machine (PSO-KELM) and long short-term memory (LSTM)-based methods. This innovative approach not only provides prediction intervals but also associates them with corresponding probabilities, offering significant potential to enhance market participants’ decision-making and mitigate price risks.</p>

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Prediction of electricity price intervals using dynamic bayesian networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月28日

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  • Hongtao Wang

摘要

The increasing volatility of electricity prices, driven by the growing share of renewable energy, calls for new approaches. This paper proposes a dynamic Bayesian network (DBN) method for electricity price interval forecasting. The model uses predicted values of wind power generation, total power generation, and total electricity consumption, along with historical electricity prices, as inputs. The network structure is determined using a greedy search algorithm, and the model parameters are estimated through maximum likelihood estimation (MLE). By treating the predictions of wind power, total generation, and total consumption as reasoning evidence, the method employs joint tree inference to generate discrete states and posterior probabilities for electricity prices, thereby enabling interval forecasting. The DBN-based interval predictions achieve a prediction interval coverage probability (PICP) of 95.24%, a normalized average width (PINAW) of 9.25%, and an accumulated width deviation (AWD) of 0.56%. The effectiveness of the proposed method was evaluated by comparing its predictions with actual electricity prices and with results from both particle swarm optimization-kernel extreme learning machine (PSO-KELM) and long short-term memory (LSTM)-based methods. This innovative approach not only provides prediction intervals but also associates them with corresponding probabilities, offering significant potential to enhance market participants’ decision-making and mitigate price risks.