Abstract
Week Ahead Electricity Price Forecasting Using Artificial Bee Colony Optimized Extreme Learning Machine with Wavelet Decomposition
Highlights
In a deregulated competitive electricity market, an electricity price, forecasted a week ahead, is one of the most important concerns of the market participators
Three different electricity price data formats are forecasted in the following manner: 1. the electricity price in Finland during the first week of April, 2017, 2. the electricity price in Switzerland during the second week of August, 2017, 3. the electricity price in India during the third week of December, 2017, using two different input formats
Likewise Tab. 12 consists of values of set 2 metrics of electricity price forecast for Indian electricity price during the fourth week of December 2017 using modified data format and from the values obtained it can be seen that the proposed method is performing better than other methods
Summary
In a deregulated competitive electricity market, an electricity price, forecasted a week ahead, is one of the most important concerns of the market participators. Market participators can adjust their bidding, based on the forecasted price to gain more profit. Electricity price forecasting has attracted researchers across the globe over the past decade due to its need and its complexity. In [1, 2] a simple Artificial Neural Network (ANN) is used for short term electricity price forecasting. Due to the complexity of the problem, the authors combined two different tools for the forecasting model, such as wavelet transform with a neural network and other time serious predictions in which wavelet transform is used to decompose the time series data into different frequencies, to enable easy training in relation to the network [3, 5]. In [4, 6, 7] fuzzy logic is integrated with the neural network and other time series predictions to improve training and testing
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