ew 16(8): e1

Research Article

Methodology for Commercial Buildings Thermal Loads Predictive Models Based on Simulation Performance

Download445 downloads
  • @ARTICLE{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},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={3},
        number={8},
        publisher={ACM},
        journal_a={EW},
        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
    EW
    EAI
    DOI: 10.4108/eai.24-8-2015.2261063
Dimitrios-Stavros Kapetanakis1,*, Eleni Mangina1, Donal Finn1
  • 1: University College Dublin (UCD)
*Contact email: dimitrios.kapetanakis@ucdconnect.ie

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.