Short-term electricity prices forecasting in a competitive market: A neural network approach

Abstract

This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California.

Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term

“…The results obtained with the HWDA approach are provided in Figures 5-8 Tables 2 and 3 shows the comparative MAPE criterion and weekly error variance criterion results, respectively, between the HWDA approach and ten previous published methodologies, namely NN [13], FNN [11], AWNN [14], HIS [12], CNEA [15], CNN [16], WPA [19], mutual information with composite NN (MI+CNN) [22], and hybrid evolutionary algorithm (HEA) [44], indicating the enhancements as the percentage evolution between the HWDA approach and the respective comparative methodology under analysis. As mentioned in [10,21], this market has features that are difficult to forecast due to influences from dominant players, which are reflected in historical data.…”

Probabilistic day-ahead system marginal price forecasting with ANN for the Turkish electricity market

“…In economics and finance, volatility is basically a criterion used to study the risks associated with holding assets when there is an uncertainty associated with the future value of the assets [12]. A high volatility means the value of a variable has changed drastically over a period of time in either direction (increase or decrease).…”

“…Due to its easily differentiable property permitting the evaluation of weight increments via the chain rule for partial derivatives used for gradient-based error minimization in backpropagation procedures [12], 'logsig' and 'tansig', which are the most widely used transfer functions in the literature, are employed for the transfer functions of the ANN in this study. In order to decrease error randomness, training and testing procedures are repeated 10 times for each case.…”

“…This type of network has been frequently used to address the demand forecasting problem in electric networks [10,11]. Recently, ANN methodology has been utilized in dealing with the short-term price-forecasting problem as well as the demand forecast problem [12].…”

Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks

“…Each layer, therefore, consists of a specific number of computational elements, called neurons, which are connected to neurons in adjacent layers and capture complex non-linear phenomena. A sigmoid function is used in a hidden layer [31].…”