Document Type: Original Article
Faculty of Engineering, Sepidan Branch, Islamic Azad University, Sepidan, Iran
Faculty of electrical and computer engineering, Islamic Azad University of Sepidan, Sepidan, Iran
Accurate electricity market price forecasting gives a capability to make better decisions in electricity market environment when, this market is complicated due to the severe fluctuations. The main purpose of a prediction model is forecasting the future prices. For doing this, the predicted variable (as output) and historical data (as input) should be close to each other. Machine learning is known as one of the most successful way of forecasting time series. Extreme learning machine (ELM) is a feed- forward neural network with one hidden layer. Hence, in this paper an extreme learning machine has been used for predicting electricity prices in a medium term time horizon. The real data of New York City electricity market has been utilized to simulate and predict the electricity price in four seasons of the year. Finally, the findings will be compared with multi-layer perceptron (MLP) results, which proves the efficiency of the model.