Research Article
Methodology for Commercial Buildings Thermal Loads Predictive Models Based on Simulation Performance
@INPROCEEDINGS{10.4108/eai.24-8-2015.2261063, author={Dimitrios-Stavros Kapetanakis and Eleni Mangina and Donal Finn}, title={Methodology for Commercial Buildings Thermal Loads Predictive Models Based on Simulation Performance}, proceedings={Eighth EAI International Conference on Simulation Tools and Techniques}, publisher={ACM}, proceedings_a={SIMUTOOLS}, year={2015}, month={8}, keywords={commercial buildings predictive models thermal loads simulation data}, doi={10.4108/eai.24-8-2015.2261063} }- Dimitrios-Stavros Kapetanakis
Eleni Mangina
Donal Finn
Year: 2015
Methodology for Commercial Buildings Thermal Loads Predictive Models Based on Simulation Performance
SIMUTOOLS
ICST
DOI: 10.4108/eai.24-8-2015.2261063
Abstract
Commercial buildings incorporate Building Energy Management Systems (BEMS) to monitor indoor environment conditions as well as controlling Heating Ventilation and Air Conditioning (HVAC) systems. Measurements of temperature, humidity and energy consumption are typically stored within BEMS. These measurements include underlying information regarding building thermal response, which is crucial for the calculation of heating and cooling loads. Forecasting of building thermal loads can be achieved using data records from BEMS. Accurate predictions can be produced when introducing these data records to data-mining predictive models. Incomplete datasets are often acquired when extracting data from the BEMS; hence detailed representations of commercial buildings can be implemented using EnergyPlus. For the purposes of the research described in this paper, different types of commercial buildings in various climates are examined to investigate the scalability of the predictive models.
