Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR

Seunghyeon Wang, Hyeonyong Hae and Juhyung Kim
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Seunghyeon Wang: Institute for Environmental Design and Engineering, Bartlett, University College London, 14 Upper Woburn Place, London WC1H 0NN, UK
Hyeonyong Hae: Department of Economics, Hansung University, 116 Samseongyoro-16Gil, Seongbuk-Gu, Seoul 02876, Korea
Juhyung Kim: Department of Architectural Engineering, Hanyang University, 222 Wangsimni-Ro, Seungdong-Gu, Seoul 133791, Korea

Energies, 2018, vol. 11, issue 2, 1-14

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.

Keywords: CPP (Critical Peak Pricing); open data; electricity consumption prediction; GA-SVR (Genetic Algorithm-Support Vector Machine) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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