Electricity demand and price forecasting model for sustainable smart grid using comprehensive long short term memory

• Published in: International journal of sustainable engineering Vol. 14; no. 6; pp. 1714 - 1732
• Main Authors: Fatema, Israt, Kong, Xiaoying, Fang, Gengfa
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.11.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Datasets; Economic forecasting; Electric power demand; Electricity; Electricity pricing; Forecasting electricity demand and price; Long short-term memory; Lstm; Mathematical models; Performance indices; Regression analysis; Root-mean-square errors; sequence-to-sequence network; Short term; smart city; Smart grid; Support vector machines; time-series data
• ISSN: 1939-7038; 1939-7046
• DOI: 10.1080/19397038.2021.1951882

This paper proposes an electricity demand and price forecast model of the smart city large datasets using a single comprehensive Long Short-Term Memory (LSTM) based on a sequence-to-sequence network. Real electricity market data from the Australian Energy Market Operator (AEMO) is used to validate the effectiveness of the proposed model. Several simulations with different configurations are executed on actual data to produce reliable results. The validation results indicate that the devised model is a better option to forecast the electricity demand and price with an acceptably smaller error. A comparison of the proposed model is also provided with a few existing models, Support Vector Machine (SVM), Regression Tree (RT), and Neural Nonlinear Autoregressive network with Exogenous variables (NARX). Compared to SVM, RT, and NARX, the performance indices, Root Mean Square Error (RMSE) of the proposed forecasting model has been improved by 11.25%, 20%, and 33.5% respectively considering demand, and by 12.8%, 14.5%, and 47% respectively considering the price; similarly, the Mean Absolute Error (MAE) has been improved by 14%, 22.5%, and 32.5% respectively considering demand, and by 8.4%, 21% and 61% respectively considering price. Additionally, the proposed model can produce reliable forecast results without large historical datasets.