Title: Efficient residential load forecasting using deep learning approach
Authors: Rida Mubashar; Mazhar Javed Awan; Muhammad Ahsan; Awais Yasin; Vishwa Pratap Singh
Addresses: Department of Information Technology, University of Management and Technology, Lahore 54770, Pakistan ' Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan ' Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan ' Department of Computer Engineering, National University of Technology, Islamabad 44000, Pakistan ' School of Information, Communication & Technology, Guru Gobind Singh Indraprastha University, Delhi 110078, India
Abstract: Reliable and efficient working of smart grids depends on smart meters that are used for tracking electricity usage and provides' accurate, granular information that can be used for forecasting power loads. Residential load forecasting is indispensable since smart meters can now be deployed at the residential level for collecting historical data consumption of residents. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, ARIMA and Exponential Smoothing. Real data from 12 houses over a period of 3 months is used to inspect and validate the accuracy of load forecasts performed using three mentioned techniques. LSTM models, due to their higher capability of memorising large data, establish their utilisation in time series-based predictions.
Keywords: short term load forecast; residential load; power system planning; LSTM; exponential smoothing; ARIMA; deep learning.
International Journal of Computer Applications in Technology, 2022 Vol.68 No.3, pp.205 - 214
Received: 12 Apr 2021
Accepted: 11 May 2021
Published online: 18 Aug 2022 *