Towards a Benchmark for ML-based Building Load Forecasting Model Selection for a Target Building-Reference-Cited by-同舟云学术

Towards a Benchmark for ML-based Building Load Forecasting Model Selection for a Target Building

Published:2023-11-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
language:
Short-container-title:

Author:

Deng Yang¹^ORCID,Liang Rui¹^ORCID,Fan Jiaqi¹^ORCID,Li Ao¹^ORCID,Wang Dan¹^ORCID

Affiliation:

1. The Hong Kong Polytechnic University, China

Funder

RGC-CRF

ITC

RGC-GRF

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3600100.3626279

Reference5 articles.

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2. A benchmark of electric vehicle load and occupancy models for day-ahead forecasting on open charging session data

3. Y. Deng , J. Fan , 202 2. Behavior testing of load forecasting models using BuildChecks. In Proc. of ACM e-Energy ’22. Y. Deng, J. Fan, 2022. Behavior testing of load forecasting models using BuildChecks. In Proc. of ACM e-Energy ’22.

4. C. Miller , A. Kathirgamanathan , 2020. The building data genome project 2, energy meter data from the ASHRAE great energy predictor III competition. Scientific data ( 2020 ). C. Miller, A. Kathirgamanathan, 2020. The building data genome project 2, energy meter data from the ASHRAE great energy predictor III competition. Scientific data (2020).

5. L. Zhang , J. Wen , 202 1. A review of machine learning in building load prediction. Applied Energy (2021). L. Zhang, J. Wen, 2021. A review of machine learning in building load prediction. Applied Energy (2021).