Wang, S;
(2018)
Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR.
Energies
, 11
(2)
, Article 373. 10.3390/en11020373.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/en11020373 |
| Publisher version: | http://doi.org/10.3390/en11020373 |
| Language: | English |
| Additional information: | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) |
| Keywords: | CPP (Critical Peak Pricing); open data; electricity consumption prediction; GA-SVR (Genetic Algorithm-Support Vector Machine) |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10044893 |
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