Short-Term Electricity Price Forecasting Based on MPSO-BP Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 1351-1355 ◽  
Author(s):  
Yu Dong ◽  
Qiang Yang ◽  
Wen Jun Yan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Electricity Market ◽  
Optimal Solution ◽  
Electricity Price ◽  
Convergence Time ◽  
Price Forecasting ◽  
Short Term ◽  
Electricity Price Forecasting ◽  
Improved Performance

In this paper, we exploited the short-term electricity price forecasting issue by introducing a global search mechanism based on the improved particle swarm optimization (MPSO) algorithm for the neural network training. The proposed MPSO algorithm is used for the initial weights and threshold of BP neural network in the process of optimization. We then proposed a novel short-term electricity price forecasting model based on MPSO-BP neural network. The paper provides a number of examples of bidding model of the California electricity market to forecasting market clear price using BP neural network trained by MPSO. Through the comparative study of the conventional BP neural network and the proposed MPSO-BP neural network, the proposed method demonstrates improved performance in finding the optimal solution with excellent convergence time for all the simulated scenarios.

Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6514
Author(s):  
Min Yi ◽  
Wei Xie ◽  
Li Mo
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Electricity Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Short Term ◽  
Swarm Optimization ◽  
Electricity Price Forecasting ◽  
Bp Model

In the electricity market environment, the market clearing price has strong volatility, periodicity and randomness, which makes it more difficult to select the input features of artificial neural network forecasting. Although the traditional back propagation (BP) neural network has been applied early in electricity price forecasting, it has the problem of low forecasting accuracy. For this reason, this paper uses the maximum information coefficient and Pearson correlation analysis to determine the main factors affecting electricity price fluctuation as the input factors of the forecasting model. The improved particle swarm optimization algorithm, called simulated annealing particle swarm optimization (SAPSO), is used to optimize the BP neural network to establish the SAPSO-BP short-term electricity price forecasting model and the actual sample data are used to simulate and calculate. The results show that the SAPSO-BP price forecasting model has a high degree of fit and the average relative error and mean square error of the forecasting model are lower than those of the BP network model and PSO-BP model, as well as better than the PSO-BP model in terms of convergence speed and accuracy, which provides an effective method for improving the accuracy of short-term electricity price forecasting.

scholarly journals Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 460-477
Author(s):  
Sajjad Khan ◽  
Shahzad Aslam ◽  
Iqra Mustafa ◽  
Sheraz Aslam
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Electricity Market ◽  
Ensemble Empirical Mode Decomposition ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Mode Decomposition ◽  
Electricity Price Forecasting ◽  
Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

Day Ahead Electricity Price Forecasting in Coupled Markets

2021 ◽  
pp. 1-29
Author(s):  
Konstantinia Daskalou ◽  
Christina Diakaki
Keyword(s):  
Neural Network ◽  
European Union ◽  
Artificial Neural Network ◽  
Electricity Market ◽  
Market Integration ◽  
Electricity Price ◽  
Price Forecasting ◽  
The European Union ◽  
Electricity Price Forecasting ◽  
Market Coupling

Day ahead electricity price forecasting is an extensively studied problem, and several statistical, intelligence-based, and other techniques have been proposed in literature to address it. However, the liberalization of the electricity market taking place during the last decades and the market coupling pursued within the European Union reshape the problem and create the need to confirm the effectiveness and/or revise existing methods and solution techniques, and/or invent new approaches. Given that complete integration has not achieved yet, both relevant data and studies of forecasting considering integration are still rather sparse. It has thus been the aim of this chapter to contribute to filling this gap by focusing on and studying the market integration effects in day ahead electricity price forecasting. To this end, an artificial neural network has been developed and used under several, with respect to inputs, forecasting scenarios considering the Italian electricity market.

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