Medium term electricity price forecasting using extreme learning machine

Accurate electricity price forecasting gives a capability to make better decisions in the electricity market environment when this market is complicated due to severe fluctuations. The main purpose of a predic tion model is to forecast 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 ways 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 are compared with multi-layer perceptron (MLP) results, which prove the efficiency of the model.