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.
Highlights
The main objective of the current paper is to describe the development of research regarding the construction of predictive models able to forecast thermal loads of commercial buildings
The methodology developed is based on the primary objective of this research, which is the determination of a predictive model able to forecast thermal loads of any given commercial building
Simulation software will be one of the milestones of this project, since the generation of the necessary database for the development of the predictive models is achieved with the usage of EnergyPlus
Summary
The building sector consumes 35% of global final energy use and is responsible for about 17% of total direct energy-related CO2 emissions from final energy consumers [1]. This research project focuses on a novel approach for costeffective modelling of measured data from commercial buildings, utilising simulation tools such as EnergyPlus [8] and IBM SPSS Modeler [9], with the implementation of machine learning prediction methods that can be assembled rapidly and deployed This approach will constitute a practical research testbed to optimise multiple objectives related to the buildings’ energy modelling research area, such as the development of a predictive model for heating and cooling loads of commercial buildings; the generation of highly accurate predictions; the scalability of the new approach to any commercial building and minimum commissioning and maintenance effort requirements. The main objective of the current paper is to describe the development of research regarding the construction of predictive models able to forecast thermal loads of commercial buildings
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