Prediction model of short-Term electrical load in an air conditioning environment
Conference proceedings article
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Publication Details
Author list: Palapanyakul K., Siripongwutikorn P.
Publisher: Hindawi
Publication year: 2017
ISBN: 9781509046669
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
Abstract
In a building office, an air-conditioning system is one of the systems that contributes most to the electrical energy expense. The ability to predict the short-Term electrical energy consumption in an air-conditioning environment can provide valuable information in controlling electrical appliance usages so that the overall energy consumption can be kept at an acceptable level for most of the time. In this paper, we apply data mining techniques to the short-Term prediction of energy consumption in air-conditioning rooms typically found in an office building. Energy consumption data and related variables in actual airconditioning environments are collected, preprocessed, and fitted to three different models, including Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Bagged Decision Tree (BDT). Unlike previous works that use only temperature and humidity as predictors, we include additional factors such as room size and BTU of air-conditioning units to improve the prediction accuracy. Our results show that the highest accuracy is achieved by using the ANN model with all the predictors included. ฉ 2017 IEEE.
Keywords
Bagged decision tree, Multiple linear regression, Short-Term electrical load prediction
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