Hourly Electricity Load Forecasting in Smart Grid Using Deep Learning Techniques
| Autor: | Abdul Basit Majeed Khan, Majid Hameed Khan, Maheen Zahid, Nadeem Javaid, Mariam Akbar, Orooj Nazeer |
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| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science 020209 energy Deep learning Feature extraction Feature selection 02 engineering and technology Mutual information computer.software_genre Convolutional neural network Smart grid Kernel (statistics) 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer |
| Zdroj: | Innovative Mobile and Internet Services in Ubiquitous Computing ISBN: 9783030222628 IMIS |
| Popis: | In this paper, a Deep Learning (DL) technique is introduced to forecast the electricity load accurately. We are facing energy shortage in today’s world. So, it is the need of the hour that proper scenario should be introduced to overcome this issue. For this purpose, moving towards Smart Grids (SG) from Traditional Grids (TG) is required. Electricity load is a factor which plays a major role in forecasting. For this purpose, we proposed a model which is based on selection, extraction and classification of historical data. Grey Correlation based Random Forest (RF) and Mutual Information (MI) is performed for feature selection, Kernel Principle Component Analysis (KPCA) is used for feature extraction and enhanced Convolutional Neural Network (CNN) is used for classification. Our proposed scheme is then compared with other benchmark schemes. Simulation results proved the efficiency and accuracy of the proposed model for hourly load forecasting of one day, one week and one month. |
| Databáze: | OpenAIRE |
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