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

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 t...