Regular Article

A multi-step electricity prediction model for residential buildings based on ensemble Empirical Mode Decomposition technique

¹ Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Bhadson Rd, Adarsh Nagar, Prem Nagar, Patiala, Punjab 147004, India
² Computer Application Department, National Institute of Technology, Kurukshetra, Haryana 136119, India

^* Corresponding author: skaur60_phd19@thapar.edu

Received: 19 August 2023
Accepted: 3 January 2024

Abstract

Residential electricity demand is increasing rapidly, constituting about a quarter of total energy consumption. Electricity demand prediction is one of the sustainable solutions to improve energy efficiency in real-world scenarios. The non-linear and non-stationary consumption patterns in residential buildings make electricity prediction more challenging. This paper proposes a multi-step prediction approach that first conducts cluster analysis to identify seasonal consumption patterns. Secondly, an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method and autoencoder model has been deployed to remove irregular patterns, noise, and redundancy from electricity load time series. Finally, the Long Short-Term Memory (LSTM) model has been trained to predict electricity consumption by considering historical, seasonal, and temporal data dependencies. Further, experimental analysis has been conducted on real-time electricity consumption datasets of residential buildings. The comparative results reveal that the proposed multi-step model outperformed the existing state-of-the-art RF-LSTM-based prediction model and attained higher accuracy.