Big Data Analytics for Price and Load Forecasting in Smart Grids

Abstract

This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load that is difficult to process with conventional computational models, referred as big data. The processing and analyzing of big data divulges the deeper insights that help experts in improvement of smart grid operations. Processing and extracting of the meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand on big data using deeper Long Short-Term Memory (LSTM). Due to adaptive and automatic feature learning of DNNs, processing of big data is easier with LSTM as compared to purely data driven methods. The proposed model is evaluated using a well-known real electricity markets’ data.