Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid

Shahzad Aslam, Nasir Ayub, Umer Farooq, Muhammad Junaid Alvi, Fahad R. Albogamy, Gul Rukh, Syed Irtaza Haider, Ahmad Taher Azar and Rasool Bukhsh
Additional contact information
Shahzad Aslam: Department of Statistics and Mathematics, Institute of Southern Punjab, Multan 66000, Pakistan
Nasir Ayub: School of Electrical Engineering and Computer Science, National University of Science and Technology, Islamabad 44000, Pakistan
Umer Farooq: Department of Computer Science, Islamia University Bahawalpur, Bahawalpur 63100, Pakistan
Muhammad Junaid Alvi: Electrical Engineering Department, NFC Institute of Engineering and Fertilizer Research, Faisalabad 38000, Pakistan
Fahad R. Albogamy: Computer Sciences Program, Turabah University College, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Gul Rukh: Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
Syed Irtaza Haider: College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Ahmad Taher Azar: College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Rasool Bukhsh: Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad 44000, Pakistan

Sustainability, 2021, vol. 13, issue 22, 1-28

Abstract: Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also a problem to be addressed. Both consumers and utilities can benefit from a predictive study of electricity demand and pricing. In this study, we used a new machine learning approach called AdaBoost to identify key features from an ISO-NE dataset that includes daily consumption data over eight years. Moreover, the DT classifier and RF are widely used to extract the best features from the dataset. Moreover, we predicted the electricity load and price using machine learning techniques including support vector machine (SVM) and deep learning techniques such as a convolutional neural network (CNN). Coronavirus herd immunity optimization (CHIO), a novel optimization approach, was used to modify the hyperparameters to increase efficiency, and it used classifiers to improve the performance of our classifier. By adding additional layers to the CNN and fine-tuning its parameters, the probability of overfitting the classifier was reduced. For method validation, we compared our proposed models with several benchmarks. MAE, MAPE, MSE, RMSE, the f1 score, recall, precision, and accuracy were the measures used for performance evaluation. Moreover, seven different forms of statistical analysis were given to show why our proposed approaches are preferable. The proposed CNN-CHIO and SVM techniques had the lowest MAPE error rates of 6% and 8%, respectively, and the highest accuracy rates of 95% and 92%, respectively.

Keywords: smart grid; electricity price forecasting; energy management; electricity load forecasting; convolutional neural network; corona virus herd immunity optimization (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 (9)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/22/12653/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/22/12653/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:22:p:12653-:d:680508

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().