A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA
Electricity demand forecasting is a term used for prediction of users’ con-sumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its...
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| Format: | Article |
| Language: | English |
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Polish Association for Knowledge Promotion
2020-03-01
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| Series: | Applied Computer Science |
| Subjects: | |
| Online Access: | http://acs.pollub.pl/pdf/v16n1/1.pdf |
| _version_ | 1818084736906559488 |
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| author | Saheed A. ADEWUYI Segun AINA Adeniran I. OLUWARANTI |
| author_facet | Saheed A. ADEWUYI Segun AINA Adeniran I. OLUWARANTI |
| author_sort | Saheed A. ADEWUYI |
| collection | DOAJ |
| description | Electricity demand forecasting is a term used for prediction of users’ con-sumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to computer vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load forecasting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem. |
| first_indexed | 2024-12-10T19:58:38Z |
| format | Article |
| id | doaj.art-2e2744c33baa4b8fb925dc7f5c60d181 |
| institution | Directory Open Access Journal |
| issn | 1895-3735 2353-6977 |
| language | English |
| last_indexed | 2024-12-10T19:58:38Z |
| publishDate | 2020-03-01 |
| publisher | Polish Association for Knowledge Promotion |
| record_format | Article |
| series | Applied Computer Science |
| spelling | doaj.art-2e2744c33baa4b8fb925dc7f5c60d1812022-12-22T01:35:34ZengPolish Association for Knowledge PromotionApplied Computer Science1895-37352353-69772020-03-0116151710.23743/acs-2020-01A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATASaheed A. ADEWUYI0Segun AINA1https://orcid.org/0000-0001-5080-1760Adeniran I. OLUWARANTI2https://orcid.org/0000-0003-4920-6053Osun State University, Department of Information and Communication Technology, Osogbo, Osun State, Nigeria, saheed.adewuyi@uniosun.edu.ngObafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State, Nigeria, s.aina@oauife.edu.ngObafemi Awolowo University, Department of Computer Science and Engineering, Ile-Ife, Osun State, NigeriaElectricity demand forecasting is a term used for prediction of users’ con-sumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to computer vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load forecasting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem.http://acs.pollub.pl/pdf/v16n1/1.pdfelectricity demand forecastingstlfdeep learning techniqueslstmcnnmlp |
| spellingShingle | Saheed A. ADEWUYI Segun AINA Adeniran I. OLUWARANTI A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA Applied Computer Science electricity demand forecasting stlf deep learning techniques lstm cnn mlp |
| title | A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA |
| title_full | A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA |
| title_fullStr | A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA |
| title_full_unstemmed | A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA |
| title_short | A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA |
| title_sort | deep learning model for electricity demand forecasting based on a tropical data |
| topic | electricity demand forecasting stlf deep learning techniques lstm cnn mlp |
| url | http://acs.pollub.pl/pdf/v16n1/1.pdf |
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