An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy
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 ener...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-05-01
|
Series: | Sustainability |
Subjects: | |
Online Access: | https://www.mdpi.com/2071-1050/13/11/6199 |
id |
doaj-c66b29c1036c4ce1884d6a61eaa789db |
---|---|
record_format |
Article |
spelling |
doaj-c66b29c1036c4ce1884d6a61eaa789db2021-06-01T01:47:29ZengMDPI AGSustainability2071-10502021-05-01136199619910.3390/su13116199An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration StrategyAdnan Yousaf0Rao Muhammad Asif1Mustafa Shakir2Ateeq Ur Rehman3Mohmmed S. Adrees4Department of Electrical Engineering, Superior University, Lahore 54000, PakistanDepartment of Electrical Engineering, Superior University, Lahore 54000, PakistanDepartment of Electrical Engineering, Superior University, Lahore 54000, PakistanDepartment of Electrical Engineering, Government College University, Lahore 54000, PakistanCollege of Computer Science and Information Technology, Al Baha University, Al Baha 1988, Saudi ArabiaLoad 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.https://www.mdpi.com/2071-1050/13/11/6199binary genetic algorithmprincipal component analysismean absolute percentage errorload forecasting |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Adnan Yousaf Rao Muhammad Asif Mustafa Shakir Ateeq Ur Rehman Mohmmed S. Adrees |
spellingShingle |
Adnan Yousaf Rao Muhammad Asif Mustafa Shakir Ateeq Ur Rehman Mohmmed S. Adrees An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy Sustainability binary genetic algorithm principal component analysis mean absolute percentage error load forecasting |
author_facet |
Adnan Yousaf Rao Muhammad Asif Mustafa Shakir Ateeq Ur Rehman Mohmmed S. Adrees |
author_sort |
Adnan Yousaf |
title |
An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy |
title_short |
An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy |
title_full |
An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy |
title_fullStr |
An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy |
title_full_unstemmed |
An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy |
title_sort |
improved residential electricity load forecasting using a machine-learning-based feature selection approach and a proposed integration strategy |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2021-05-01 |
description |
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. |
topic |
binary genetic algorithm principal component analysis mean absolute percentage error load forecasting |
url |
https://www.mdpi.com/2071-1050/13/11/6199 |
work_keys_str_mv |
AT adnanyousaf animprovedresidentialelectricityloadforecastingusingamachinelearningbasedfeatureselectionapproachandaproposedintegrationstrategy AT raomuhammadasif animprovedresidentialelectricityloadforecastingusingamachinelearningbasedfeatureselectionapproachandaproposedintegrationstrategy AT mustafashakir animprovedresidentialelectricityloadforecastingusingamachinelearningbasedfeatureselectionapproachandaproposedintegrationstrategy AT ateequrrehman animprovedresidentialelectricityloadforecastingusingamachinelearningbasedfeatureselectionapproachandaproposedintegrationstrategy AT mohmmedsadrees animprovedresidentialelectricityloadforecastingusingamachinelearningbasedfeatureselectionapproachandaproposedintegrationstrategy AT adnanyousaf improvedresidentialelectricityloadforecastingusingamachinelearningbasedfeatureselectionapproachandaproposedintegrationstrategy AT raomuhammadasif improvedresidentialelectricityloadforecastingusingamachinelearningbasedfeatureselectionapproachandaproposedintegrationstrategy AT mustafashakir improvedresidentialelectricityloadforecastingusingamachinelearningbasedfeatureselectionapproachandaproposedintegrationstrategy AT ateequrrehman improvedresidentialelectricityloadforecastingusingamachinelearningbasedfeatureselectionapproachandaproposedintegrationstrategy AT mohmmedsadrees improvedresidentialelectricityloadforecastingusingamachinelearningbasedfeatureselectionapproachandaproposedintegrationstrategy |
_version_ |
1721411616617005056 |