Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

Sana Mujeeb, Nadeem Javaid, Manzoor Ilahi, Zahid Wadud, Farruh Ishmanov and Muhammad Khalil Afzal
Additional contact information
Sana Mujeeb: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Nadeem Javaid: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Manzoor Ilahi: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Zahid Wadud: Department of Computer System Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
Farruh Ishmanov: Department of Electronics and Communication Engineering, Kwangwoon University, Seoul 01897, Korea
Muhammad Khalil Afzal: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantonment 47040, Pakistan

Sustainability, 2019, vol. 11, issue 4, 1-29

Abstract: This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of 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 for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.

Keywords: smart grid; big data; electricity load; price forecasting; Long Short-Term Memory LSTM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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