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Demand forecasting for a Mixed-Use Building using an Agent-schedule information Data-Driven Model
- Source :
- Li, Z, Friedrich, D & Harrison, G 2020, ' Demand forecasting for a Mixed-Use Building using an Agent-schedule information Data-Driven Model ', Energies, vol. 13, no. 4, 780 . https://doi.org/10.3390/en13040780, Energies, Vol 13, Iss 4, p 780 (2020), Energies; Volume 13; Issue 4; Pages: 780
- Publication Year :
- 2020
-
Abstract
- There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of ‘agent schedules’ that provide proxy information that is based on broad classes of activity undertaken by the building’s inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the ‘agent schedules’, which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all ‘agent schedule’ features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction.
- Subjects :
- Schedule
Mathematical optimization
Control and Optimization
Computer science
020209 energy
Energy Engineering and Power Technology
Feature selection
02 engineering and technology
010501 environmental sciences
Thermal load
01 natural sciences
lcsh:Technology
demand prediction
Genetic algorithm
0202 electrical engineering, electronic engineering, information engineering
Feature (machine learning)
Electrical and Electronic Engineering
Engineering (miscellaneous)
electricity demand
0105 earth and related environmental sciences
data driven
buildings
thermal demand
Renewable Energy, Sustainability and the Environment
business.industry
lcsh:T
Regression analysis
Energy consumption
Demand forecasting
Electricity
business
Energy (miscellaneous)
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Li, Z, Friedrich, D & Harrison, G 2020, ' Demand forecasting for a Mixed-Use Building using an Agent-schedule information Data-Driven Model ', Energies, vol. 13, no. 4, 780 . https://doi.org/10.3390/en13040780, Energies, Vol 13, Iss 4, p 780 (2020), Energies; Volume 13; Issue 4; Pages: 780
- Accession number :
- edsair.doi.dedup.....d84c07f2fd26f068a0693962eb3870e2