Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR
In many countries, DR (Demand Response) has been developed for which customers are motivated to save electricity by themselves during peak time to prevent grand-scale blackouts. One of the common methods in DR, is CPP (Critical Peak Pricing). Predicting energy consumption is recognized as one of the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-02-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/11/2/373 |
| _version_ | 1818015125476474880 |
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| author | Seunghyeon Wang Hyeonyong Hae Juhyung Kim |
| author_facet | Seunghyeon Wang Hyeonyong Hae Juhyung Kim |
| author_sort | Seunghyeon Wang |
| collection | DOAJ |
| description | In many countries, DR (Demand Response) has been developed for which customers are motivated to save electricity by themselves during peak time to prevent grand-scale blackouts. One of the common methods in DR, is CPP (Critical Peak Pricing). Predicting energy consumption is recognized as one of the tool for dealing with CPP. There are a variety of studies in developing the model of energy consumption, which is based on energy simulation, data-driven model or metamodelling. However, it is difficult for general users to use these models due to requirement of various sensing data and expertise. And it also takes long time to simulate the models. These limitations can be an obstacle for achieving CPP’s purpose that encourages general users to manage their energy usage by themselves. As an alternative, this research suggests to use open data and GA (Genetic Algorithm)–SVR (Support Vector Regression). The model is applied to a hospital in Korea and 34,636 data sets (1 year) are collected while 31,756 (11 months) sets are used for training and 2880 sets (1 month) are used for validation. As a result, the performance of proposed model is 14.17% in CV (RMSE), which satisfies the Korea Energy Agency’s and ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) error allowance range of ±30%, and ±20% respectively. |
| first_indexed | 2024-04-14T06:52:54Z |
| format | Article |
| id | doaj.art-ffe38ee8832d4a01a515fae5652739b2 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T06:52:54Z |
| publishDate | 2018-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ffe38ee8832d4a01a515fae5652739b22022-12-22T02:06:58ZengMDPI AGEnergies1996-10732018-02-0111237310.3390/en11020373en11020373Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVRSeunghyeon Wang0Hyeonyong Hae1Juhyung Kim2Institute for Environmental Design and Engineering, Bartlett, University College London, 14 Upper Woburn Place, London WC1H 0NN, UKDepartment of Economics, Hansung University, 116 Samseongyoro-16Gil, Seongbuk-Gu, Seoul 02876, KoreaDepartment of Architectural Engineering, Hanyang University, 222 Wangsimni-Ro, Seungdong-Gu, Seoul 133791, KoreaIn many countries, DR (Demand Response) has been developed for which customers are motivated to save electricity by themselves during peak time to prevent grand-scale blackouts. One of the common methods in DR, is CPP (Critical Peak Pricing). Predicting energy consumption is recognized as one of the tool for dealing with CPP. There are a variety of studies in developing the model of energy consumption, which is based on energy simulation, data-driven model or metamodelling. However, it is difficult for general users to use these models due to requirement of various sensing data and expertise. And it also takes long time to simulate the models. These limitations can be an obstacle for achieving CPP’s purpose that encourages general users to manage their energy usage by themselves. As an alternative, this research suggests to use open data and GA (Genetic Algorithm)–SVR (Support Vector Regression). The model is applied to a hospital in Korea and 34,636 data sets (1 year) are collected while 31,756 (11 months) sets are used for training and 2880 sets (1 month) are used for validation. As a result, the performance of proposed model is 14.17% in CV (RMSE), which satisfies the Korea Energy Agency’s and ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) error allowance range of ±30%, and ±20% respectively.http://www.mdpi.com/1996-1073/11/2/373CPP (Critical Peak Pricing)open dataelectricity consumption predictionGA-SVR (Genetic Algorithm-Support Vector Machine) |
| spellingShingle | Seunghyeon Wang Hyeonyong Hae Juhyung Kim Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR Energies CPP (Critical Peak Pricing) open data electricity consumption prediction GA-SVR (Genetic Algorithm-Support Vector Machine) |
| title | Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR |
| title_full | Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR |
| title_fullStr | Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR |
| title_full_unstemmed | Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR |
| title_short | Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR |
| title_sort | development of easily accessible electricity consumption model using open data and ga svr |
| topic | CPP (Critical Peak Pricing) open data electricity consumption prediction GA-SVR (Genetic Algorithm-Support Vector Machine) |
| url | http://www.mdpi.com/1996-1073/11/2/373 |
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