The Hourly Energy Consumption Prediction by KNN for Buildings in Community Buildings

Hong, Goopyo; Choi, Gyeong-Seok; Eum, Ji-Young; Lee, Han Sol; Kim, Daeung Danny

doi:10.3390/buildings12101636

Open AccessArticle

The Hourly Energy Consumption Prediction by KNN for Buildings in Community Buildings

by

Goopyo Hong

¹,

Gyeong-Seok Choi

²

,

Ji-Young Eum

²

,

Han Sol Lee

² and

Daeung Danny Kim

^3,*

¹

Department of Architectural Engineering, Kangwon National University, Samcheok 25913, Korea

²

Department of Building Energy Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Korea

³

Architectural Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia

^*

Author to whom correspondence should be addressed.

Buildings 2022, 12(10), 1636; https://doi.org/10.3390/buildings12101636

Submission received: 8 September 2022 / Revised: 3 October 2022 / Accepted: 7 October 2022 / Published: 9 October 2022

(This article belongs to the Special Issue Building Energy and Sustainability)

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Abstract

With the development of metering technologies, data mining techniques such as machine learning have been increasingly used for the prediction of building energy consumption. Among various machine learning methods, the KNN algorithm was implemented to predict the hourly energy consumption of community buildings composed of several different types of buildings. Based on the input data set, 10 similar hourly energy patterns for each season in the historic data sets were chosen, and these 10 energy consumption patterns were averaged. The prediction results were analyzed quantitatively and qualitatively. The prediction results for the summer and fall were close to the energy consumption data, while the results for the spring and winter were higher than the energy consumption data. For accuracy, a similar trend was observed. The values of CVRMSE for the summer and fall were within the acceptable range of ASHRAE guidelines 14, while higher values of CVRMSE for the spring and winter were observed. In sum, the total values of CVRMSE were within the acceptable range.

Keywords: hourly energy consumption; KNN algorithm; energy pattern; community buildings

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MDPI and ACS Style

Hong, G.; Choi, G.-S.; Eum, J.-Y.; Lee, H.S.; Kim, D.D. The Hourly Energy Consumption Prediction by KNN for Buildings in Community Buildings. Buildings 2022, 12, 1636. https://doi.org/10.3390/buildings12101636

AMA Style

Hong G, Choi G-S, Eum J-Y, Lee HS, Kim DD. The Hourly Energy Consumption Prediction by KNN for Buildings in Community Buildings. Buildings. 2022; 12(10):1636. https://doi.org/10.3390/buildings12101636

Chicago/Turabian Style

Hong, Goopyo, Gyeong-Seok Choi, Ji-Young Eum, Han Sol Lee, and Daeung Danny Kim. 2022. "The Hourly Energy Consumption Prediction by KNN for Buildings in Community Buildings" Buildings 12, no. 10: 1636. https://doi.org/10.3390/buildings12101636

APA Style

Hong, G., Choi, G.-S., Eum, J.-Y., Lee, H. S., & Kim, D. D. (2022). The Hourly Energy Consumption Prediction by KNN for Buildings in Community Buildings. Buildings, 12(10), 1636. https://doi.org/10.3390/buildings12101636

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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The Hourly Energy Consumption Prediction by KNN for Buildings in Community Buildings

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