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.
Highlights
Electric consumption is on the increase, which is turning out to be the woes of many countries.DR (Demand Response) has been introduced by which the customers in the electricity market are motivated to save electricity during peak time for preventing grand-scale blackouts from the unconscious use of electricity [1,2,3]
There are a variety of studies in developing the model of energy consumption [6,7,8,9,10,11,12,13,14], which is based on energy simulation or data-driven model, and metamodeling
As energy data related to energy consumption such as temperature and humidity shows nonlinear patterns, making good a model requires the best prediction method that is capable of recognizing the data [17]
Summary
Electric consumption is on the increase, which is turning out to be the woes of many countries. It is difficult for general user to use these models due to requirement of various sensing data and expertise. It takes a long time to generate energy consumption model. Since policy of releasing open data is different, the empowerment of general users and type of open data vary [15,16]. As energy data related to energy consumption such as temperature and humidity shows nonlinear patterns, making good a model requires the best prediction method that is capable of recognizing the data [17]. If the proposed approach can be employed, by following this, Korean and other general users that can gain open data from their government can implement energy consumption model without the difficulties (expensive sensor, professional expertise, and long simulation time) for responding to CPP. Literature Review on the Necessity of Proposed Approach and Design for Model Based on Machine Learning
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