IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i17p4361-d403358.html
   My bibliography  Save this article

Forecasting the Energy Consumption of an Actual Air Handling Unit and Absorption Chiller Using ANN Models

Author

Listed:
  • Jee-Heon Kim

    (Eco-System Research Center, Gachon University, Seongnam 13120, Korea)

  • Nam-Chul Seong

    (Eco-System Research Center, Gachon University, Seongnam 13120, Korea)

  • Wonchang Choi

    (Department of Architectural Engineering, Gachon University, Seongnam 13120, Korea)

Abstract

Air conditioning in buildings accounts for 60% of the total energy consumption. Therefore, accurate predictions of energy consumption are needed to properly manage the energy consumption of buildings. For this purpose, many studies have been conducted recently on the prediction of energy consumption of buildings using machine learning techniques. The energy consumption of the air handling unit (AHU) and absorption chiller in an actual building’s air conditioning system is predicted in this paper using prediction models that are based on artificial neural networks (ANNs), which simply and accurately allow us to forecast energy consumption with limited variables. Using these ANN models, the energy usage of the AHU and chiller could be predicted by collecting a month’s worth of driving data during the summer cooling period. After the forecast models had been verified, the AHU prediction model showed performance in the ranges of 13.27% to 15.25% and 19.42% to 19.53% for the training period and testing period, respectively, and the mean bias error (MBE) ranges were 4.03% to 4.97% and 3.48% to 4.39% for the training period and testing period, respectively. The chiller prediction model satisfied the energy consumption forecast performance criteria presented by American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) guideline 14 (the measurement of energy and demand savings), with a performance of 24.64~25.58% and 7.12~29.39% in the training period and testing period, respectively, and MBE ranges of 2.59~3.40% and 1.35~2.87% in the training period and testing period, respectively. When the training period and testing period were combined for the AHU data, the actual energy usage forecast showed a lower error rate range of 0.22% to 1.11% for the training period and 0.17% to 2.44% for the testing period. For the chiller data, the error rate range was 0.22% to 2.12% for the entire training period, but was somewhat higher at 11.67% to 15.18% for the testing period. The study found that, even if the performance criteria were met, high accuracy results were not obtained, which was due to the poor data set quality. Although the forecast model based on artificial neural network can achieve relatively high-accuracy results with sufficient amounts of data, it is believed that this will require a thorough verification of the data used, as well as improvements in the predictive model to avoid overfitting and underfitting, to achieve such good results.

Suggested Citation

  • Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2020. "Forecasting the Energy Consumption of an Actual Air Handling Unit and Absorption Chiller Using ANN Models," Energies, MDPI, vol. 13(17), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4361-:d:403358
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/17/4361/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/17/4361/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    2. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm," Energies, MDPI, vol. 12(15), pages 1-13, July.
    3. Lizana, Jesús & Chacartegui, Ricardo & Barrios-Padura, Angela & Valverde, José Manuel, 2017. "Advances in thermal energy storage materials and their applications towards zero energy buildings: A critical review," Applied Energy, Elsevier, vol. 203(C), pages 219-239.
    4. Le Cam, M. & Daoud, A. & Zmeureanu, R., 2016. "Forecasting electric demand of supply fan using data mining techniques," Energy, Elsevier, vol. 101(C), pages 541-557.
    5. Zina Boussaada & Octavian Curea & Ahmed Remaci & Haritza Camblong & Najiba Mrabet Bellaaj, 2018. "A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation," Energies, MDPI, vol. 11(3), pages 1-21, March.
    6. Koschwitz, D. & Frisch, J. & van Treeck, C., 2018. "Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A comparative study on district scale," Energy, Elsevier, vol. 165(PA), pages 134-142.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zahra Jahangiri & Mackenzie Judson & Kwang Moo Yi & Madeleine McPherson, 2023. "A Deep Learning Approach for Exploring the Design Space for the Decarbonization of the Canadian Electricity System," Energies, MDPI, vol. 16(3), pages 1-21, January.
    2. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    2. SeyedAli Ghahari & Cesar Queiroz & Samuel Labi & Sue McNeil, 2021. "Cluster Forecasting of Corruption Using Nonlinear Autoregressive Models with Exogenous Variables (NARX)—An Artificial Neural Network Analysis," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
    3. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    4. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    5. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    6. Anshuman Satapathy & Niranjan Nayak & Tanmoy Parida, 2022. "Real-Time Power Quality Enhancement in a Hybrid Micro-Grid Using Nonlinear Autoregressive Neural Network," Energies, MDPI, vol. 15(23), pages 1-35, November.
    7. Li, Wei & Klemeš, Jiří Jaromír & Wang, Qiuwang & Zeng, Min, 2020. "Development and characteristics analysis of salt-hydrate based composite sorbent for low-grade thermochemical energy storage," Renewable Energy, Elsevier, vol. 157(C), pages 920-940.
    8. Singh, Aditya Kumar & Rathore, Pushpendra Kumar Singh & Sharma, R.K. & Gupta, Naveen Kumar & Kumar, Rajan, 2023. "Experimental evaluation of composite concrete incorporated with thermal energy storage material for improved thermal behavior of buildings," Energy, Elsevier, vol. 263(PA).
    9. Oksana Mandrikova & Yuryi Polozov & Nataly Zhukova & Yulia Shichkina, 2022. "Approximation and Analysis of Natural Data Based on NARX Neural Networks Involving Wavelet Filtering," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    10. Sun, Xiaoqin & Medina, Mario A. & Lee, Kyoung Ok & Jin, Xing, 2018. "Laboratory assessment of residential building walls containing pipe-encapsulated phase change materials for thermal management," Energy, Elsevier, vol. 163(C), pages 383-391.
    11. Khor, J.O. & Sze, J.Y. & Li, Y. & Romagnoli, A., 2020. "Overcharging of a cascaded packed bed thermal energy storage: Effects and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    12. Guilherme Henrique Alves & Geraldo Caixeta Guimarães & Fabricio Augusto Matheus Moura, 2023. "Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing," Energies, MDPI, vol. 16(14), pages 1-30, July.
    13. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
    14. Ilaria Vigna & Jessica Balest & Wilmer Pasut & Roberta Pernetti, 2020. "Office Occupants’ Perspective Dealing with Energy Flexibility: A Large-Scale Survey in the Province of Bolzano," Energies, MDPI, vol. 13(17), pages 1-20, August.
    15. Jaroslaw Krzywanski, 2019. "A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods," Energies, MDPI, vol. 12(23), pages 1-32, November.
    16. Yu, De-Hai & He, Zhi-Zhu, 2019. "Shape-remodeled macrocapsule of phase change materials for thermal energy storage and thermal management," Applied Energy, Elsevier, vol. 247(C), pages 503-516.
    17. Vlad Mureșan & Mihaela-Ligia Ungureșan & Mihail Abrudean & Honoriu Vălean & Iulia Clitan & Roxana Motorga & Emilian Ceuca & Marius Fișcă, 2021. "AI versus Classic Methods in Modelling Isotopic Separation Processes: Efficiency Comparison," Mathematics, MDPI, vol. 9(23), pages 1-31, November.
    18. Palomba, Valeria & Sapienza, Alessio & Aristov, Yuri, 2019. "Dynamics and useful heat of the discharge stage of adsorptive cycles for long term thermal storage," Applied Energy, Elsevier, vol. 248(C), pages 299-309.
    19. Liu, Yang & Wang, Hongxia & Ayub, Iqra & Yang, Fusheng & Wu, Zhen & Zhang, Zaoxiao, 2021. "A variable cross-section annular fins type metal hydride reactor for improving the phenomenon of inhomogeneous reaction in the thermal energy storage processes," Applied Energy, Elsevier, vol. 295(C).
    20. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4361-:d:403358. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.