Energy consumption prediction and energy-saving suggestions of public buildings based on machine learning
Authors:
- Cheng ChenCCCheng Chen
- Zhiming GaoZGZhiming Gao
- Xuan ZhouXZXuan Zhou
- Miao WangMWMiao Wang
- Junwei YanJYJunwei Yan
- 30 July 2024
- journal article
- research article
- Published by Elsevier in Energy and Buildings
- Vol. 320
Abstract
No abstract availableKeywords
This publication has 42 references indexed in Scilit:
- Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A reviewCurrent Research in Food Science, 2021
- Statistical investigations of transfer learning-based methodology for short-term building energy predictionsApplied Energy, 2020
- Deep learning-based feature engineering methods for improved building energy predictionApplied Energy, 2019
- Support vector regression for predicting building energy consumption in southern ChinaEnergy Procedia, 2019
- Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniquesApplied Energy, 2019
- Machine learning approaches for estimating commercial building energy consumptionApplied Energy, 2017
- Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technologyApplied Energy, 2016
- A hybrid model approach for forecasting future residential electricity consumptionEnergy and Buildings, 2016
- Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildingsEnergy and Buildings, 2016
- An energy benchmarking model based on artificial neural network method utilizing US Commercial Buildings Energy Consumption Survey (CBECS) databaseInternational Journal of Energy Research, 2006