We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids.
- Authors
Zahid, Maheen; Ahmed, Fahad; Javaid, Nadeem; Abbasi, Raza Abid; Zainab Kazmi, Hafiza Syeda; Javaid, Atia; Bilal, Muhammad; Akbar, Mariam; Ilahi, Manzoor
- 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).
- Subjects
ELECTRICITY pricing; LOAD forecasting (Electric power systems); ARTIFICIAL neural networks; SUPPORT vector machines; SMART power grids
- Publication
Electronics (2079-9292), 2019, Vol 8, Issue 2, p122
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics8020122