An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy

Adnan Yousaf, Rao Muhammad Asif, Mustafa Shakir, Ateeq Ur Rehman and Mohmmed S. Adrees
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Adnan Yousaf: Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan
Rao Muhammad Asif: Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan
Mustafa Shakir: Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan
Ateeq Ur Rehman: Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan
Mohmmed S. Adrees: College of Computer Science and Information Technology, Al Baha University, Al Baha 1988, Saudi Arabia

Sustainability, 2021, vol. 13, issue 11, 1-20

Abstract: Load forecasting (LF) has become the main concern in decentralized power generation systems with the smart grid revolution in the 21st century. As an intriguing research topic, it facilitates generation systems by providing essential information for load scheduling, demand-side integration, and energy market pricing and reducing cost. An intelligent LF model of residential loads using a novel machine learning (ML)-based approach, achieved by assembling an integration strategy model in a smart grid context, is proposed. The proposed model improves the LF by optimizing the mean absolute percentage error (MAPE). The time-series-based autoregression schemes were carried out to collect historical data and set the objective functions of the proposed model. An algorithm consisting of seven different autoregression models was also developed and validated through a feedforward adaptive-network-based fuzzy inference system (ANFIS) model, based on the ML approach. Moreover, a binary genetic algorithm (BGA) was deployed for the best feature selection, and the best fitness score of the features was obtained with principal component analysis (PCA). A unique decision integration strategy is presented that led to a remarkably improved transformation in reducing MAPE. The model was tested using a one-year Pakistan Residential Electricity Consumption (PRECON) dataset, and the attained results verify that the proposed model obtained the best feature selection and achieved very promising values of MAPE of 1.70%, 1.77%, 1.80%, and 1.67% for summer, fall, winter, and spring seasons, respectively. The overall improvement percentage is 17%, which represents a substantial increase for small-scale decentralized generation units.

Keywords: binary genetic algorithm; principal component analysis; mean absolute percentage error; load forecasting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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