Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids
| Autor: | Fahad Ahmed, Mariam Akbar, Hafiza Syeda Zainab Kazmi, Nadeem Javaid, Raza Abid Abbasi, Atia Javaid, Manzoor Ilahi, Maheen Zahid, Muhammad Bilal |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer Networks and Communications
Computer science 020209 energy Load forecasting Decision tree lcsh:TK7800-8360 Support Vector Regression Feature selection Convolutional Neural Network 02 engineering and technology computer.software_genre Convolutional neural network 0202 electrical engineering electronic engineering information engineering load and price forecasting Electrical and Electronic Engineering smart grid business.industry Deep learning 020208 electrical & electronic engineering lcsh:Electronics Data Analytics data mining Random forest Support vector machine Smart grid Hardware and Architecture Control and Systems Engineering Signal Processing Benchmark (computing) Electricity Data mining Artificial intelligence business computer |
| Zdroj: | Electronics Volume 8 Issue 2 Electronics, Vol 8, Iss 2, p 122 (2019) |
| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics8020122 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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