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.

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.

scholarly journals Electricity price forecasting in Turkey with artificial neural network models

2016 ◽  
Vol 13 (3) ◽  
pp. 150-158 ◽  
Author(s):  
Fazil Gökgöz ◽  
Fahrettin Filiz
Keyword(s):  
Neural Network ◽  
Electricity Market ◽  
Gradient Descent ◽  
Network Models ◽  
Electricity Price ◽  
Price Forecasting ◽  
Neural Network Models ◽  
Electricity Price Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Models

The electricity market has experienced significant changes towards deregulation with the aim of improving economic efficiency. The electricity pricing is a major consideration for consumers and generation companies in deregulated electric markets, so that offering the right price for electricity has become more important. Various methods and ideas have been tried for electricity price forecasting. Artificial neural networks have received much attention with its nonlinear property and many papers have reported successful experiments with them. This paper introduces artificial neural network models for day-ahead electricity market in Turkey. Using gradient descent, gradient descent with momentum, Broydan, Fletcher, Goldfarb and Shanno (BFGS) and Levenberg-Marquardt algorithm with different number of neuron and transfer functions, 400 different models are created. Performances of different models are compared according to their Mean Absolute Percentage (MAPE) values; the most successful models MAPE value is observed as 9.76%. Keywords: electricity price forecasting, neural networks, day-ahead electricity market, Turkey. JEL Classification: C02, C13, C45, C53

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.

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