Abstract
Nearly 40% of primary energy consumption is related to the usage of energy in Buildings. Energy-related data such as indoor air temperature and power consumption of heating/cooling systems can be now collected due to the widespread diffusion of Internet-of-Things devices. Such energy data can be used (i) to train data-driven models than learn the thermal properties of buildings and (ii) to predict indoor temperature evolution. In this paper, we present a Grey-box model to estimate thermal dynamics in buildings based on Unscented Kalman Filter and thermal network representation. The proposed methodology has been applied in two different buildings with two different thermal network discretizations to test its accuracy in indoor air temperature prediction. Due to a lack of a real-world data sampled by Internet of Things (IoT) devices, a realistic data-set has been generated using the software Energy+, by referring to real industrial building models. Results on synthetic and realistic data show the accuracy of the proposed methodology in predicting indoor temperature trends up to the next 24 h with a maximum error lower than 2 °C, considering one year of data with different weather conditions.
Highlights
Nowadays, over half of the overall world’s population lives in urban areas
In this paper, we extend our previous work [13] and present a novel data-driven model based on Unscented Kalman Filter [8] (UKF) to estimate thermal dynamics in buildings
We extended our previous work [13] by presenting a novel Grey-box model to predict indoor air temperature in buildings by learning their thermal characteristic and dynamics
Summary
Over half of the overall world’s population lives in urban areas. United Nations studies predict that by 2030 urban areas will hold about 68% of people, of which one-third would live in towns with a population of at least half a million [1]. Roughly 40% of total energy consumption [3] is related to heating and cooling systems of buildings. In this context, Information and Communication Technologies (ICT) and, in particular, Internet-of-Things (IoT) technologies play a crucial role in enabling new fined-grained algorithm to monitor and optimize energy consumption [4], increasing the efficiency of energy systems
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