Day-Ahead Market Electricity Price Prediction using Time Series Forecasting

Electricity price is an essential signal in competitive electricity market. Every participant has to make a prediction of the clearing price in order to optimize the bids thus price forecast acts as the decision making basis. This study presents a comparative analysis of diverse price forecasting methods to give a comprehensive analysis of these forecasting methods. The presented methods comprise of a conventional method i.e. statistical method- auto regressive integrated moving average, adaptive method i.e. machine learning method- random forest regression, and novel deep learning method i.e. long short term memory artificial neural network. The price forecasting is formulated as a time series forecasting problem. These techniques are used for predicting electricity price of New South Wales area of Australian electricity market and Singapore electricity market over a span of 5 years ranging from 2017–2021.