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All Journal Journal of Engineering and Technological Sciences Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Putu Handre Kertha Utama
Institut Teknologi Bandung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Journal : Journal of Engineering and Technological Sciences

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Data Driven Building Electricity Consumption Model Using Support Vector Regression FX Nugroho Soelami; Putu Handre Kertha Utama; Irsyad Nashirul Haq; Justin Pradipta; Edi Leksono; Meditya Wasesa
Journal of Engineering and Technological Sciences Vol. 53 No. 3 (2021)
Publisher : Institute for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2021.53.3.13

Abstract

Every building has certain electricity consumption patterns that depend on its usage. Building electricity budget planning requires a consumption forecast to determine the baseline electricity load and to support energy management decisions. In this study, an algorithm to model building electricity consumption was developed. The algorithm is based on the support vector regression (SVR) method. Data of electricity consumption from the past five years from a selected building object in ITB campus were used. The dataset unexpectedly exhibited a large number of anomalous points. Therefore, a tolerance limit of hourly average energy consumption was defined to obtain good quality training data. Various tolerance limits were investigated, that is 15% (Type 1), 30% (Type 2), and 0% (Type 0). The optimal model was selected based on the criteria of mean absolute percentage error (MAPE) < 20% and root mean square error (RMSE) < 10 kWh. Type 1 data was selected based on its performance compared to the other two. In a real implementation, the model yielded a MAPE value of 14.79% and an RMSE value of 7.48 kWh when predicting weekly electricity consumption. Therefore, the Type 1 data-based model could satisfactorily forecast building electricity consumption.
Co-Authors Aretha Fieradiella Pahrevi Edi Leksono Faizatuzzahrah Rahmaniah FX Nugroho Soelami Irsyad Nashirul Haq Justin Pradipta Meditya Wasesa Mochammad Iqbal Bayeqi Muhammad Fatih Hasan Reza Fauzi Iskandar Robi Sobirin