A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system

2016 ◽  
Vol 48 ◽  
pp. 281-297 ◽  
Author(s):  
Jianzhou Wang ◽  
Feng Liu ◽  
Yiliao Song ◽  
Jing Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electricity Price ◽  
Price Forecasting ◽  
Dynamic Choice ◽  
Electricity Price Forecasting ◽  
Forecasting System ◽  
Artificial Neural ◽  
Novel Model

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.

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

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