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Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model

School of Engineering, the University of Edinburgh, Edinburgh EH9 3FB, UK
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Energies 2020, 13(4), 780; https://doi.org/10.3390/en13040780
Received: 19 December 2019 / Revised: 27 January 2020 / Accepted: 6 February 2020 / Published: 11 February 2020
(This article belongs to the Section Energy and Buildings)
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. View Full-Text
Keywords: data driven; buildings; thermal demand; electricity demand; demand prediction data driven; buildings; thermal demand; electricity demand; demand prediction
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MDPI and ACS Style

Li, Z.; Friedrich, D.; Harrison, G.P. Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model. Energies 2020, 13, 780.

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