Next Article in Journal
Development of a Miniaturized Frequency Standard Comparator Based on FPGA
Previous Article in Journal
Joint Estimation of Doppler Stretch and Time Delay of Wideband Echoes for LFM Pulse Radar Based on Sigmoid-FRFT Transform under the Impulsive Noise Environment
Previous Article in Special Issue
Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
Article Menu

Export Article

Open AccessArticle
Electronics 2019, 8(2), 122; https://doi.org/10.3390/electronics8020122

Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids

1
Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
2
School of Computing and IT, Centre for Data Science and Analytics, Taylor’s University, Subang Jaya 47500, Malaysia
*
Author to whom correspondence should be addressed.
Received: 31 December 2018 / Revised: 13 January 2019 / Accepted: 14 January 2019 / Published: 23 January 2019
(This article belongs to the Special Issue Green Communications in Smart City)
Full-Text   |   PDF [1620 KB, uploaded 24 January 2019]   |  

Abstract

Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. Forecasting about electricity load and price provides future trends and patterns of consumption. There is a loss in generation and use of electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead electricity price and load forecasting are beneficial for both suppliers and consumers. In this paper, Deep Learning (DL) and data mining techniques are used for electricity load and price forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature Elimination (RFE) and Random Forest (RF) are used for feature selection and feature extraction. Enhanced Convolutional Neural Network (ECNN) and Enhanced Support Vector Regression (ESVR) are used as classifiers. Grid Search (GS) is used for tuning of the parameters of classifiers to increase their performance. The risk of over-fitting is mitigated by adding multiple layers in ECNN. Finally, the proposed models are compared with different benchmark schemes for stability analysis. The performance metrics MSE, RMSE, MAE, and MAPE are used to evaluate the performance of the proposed models. The experimental results show that the proposed models outperformed other benchmark schemes. ECNN performed well with threshold 0.08 for load forecasting. While ESVR performed better with threshold value 0.15 for price forecasting. ECNN achieved almost 2% better accuracy than CNN. Furthermore, ESVR achieved almost 1% better accuracy than the existing scheme (SVR). View Full-Text
Keywords: load and price forecasting; smart grid; Convolutional Neural Network; Support Vector Regression; Data Analytics; data mining load and price forecasting; smart grid; Convolutional Neural Network; Support Vector Regression; Data Analytics; data mining
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zahid, M.; Ahmed, F.; Javaid, N.; Abbasi, R.A.; Zainab Kazmi, H.S.; Javaid, A.; Bilal, M.; Akbar, M.; Ilahi, M. Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids. Electronics 2019, 8, 122.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top