A Grey-box Model Based on Unscented Kalman Filter to Estimate Thermal Dynamics in Buildings

Massano, Marco; Macii, Enrico; Patti, Edoardo; Acquaviva, Andrea; Bottaccioli, Lorenzo

doi:10.1109/eeeic.2019.8783974

Cited by 11 publications

(19 citation statements)

References 19 publications

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“…The main contribution of the paper consists on testing different Grey-box models of buildings and on using the Unscented Kalman Filter technique to learn and predict building heating/cooling dynamics. This paper presents several new insights and extensions, both methodological and analytical, in relation to our previous work [13]. As in the previous article, also in this case, synthetic data have been used to test the method.…”

Section: Introductionmentioning

confidence: 79%

“…On these premises, in this paper, we extend our previous work [13] and present a novel data-driven model based on UKF to estimate thermal dynamics in buildings. It takes advantage of information sampled by pervasive IoT devices to allow control policies for: (i) Optimal System Operating Scheduling, (ii) Model Predictive Control, (iii) Demand Side Management and (iv) Demand/Response.…”

Section: Introductionmentioning

confidence: 84%

“…Therefore, we have analyzed the performance of the proposed multi-filter algorithm on a 17-zone RC-model, increasing the building model complexity w.r.t. [13], that was up to 5 zones. Moreover, with respect to our previous work, we analyzed the difference in treating the heat gain of the HVAC (Heating, Ventilation and Air Conditioning) system as an input to the model or as a disturbance.…”

Section: Introductionmentioning

confidence: 95%

“…Finally, in our previous work [13], we estimated the effects of solar heat gain and HVAC systems on the building indoor air temperature evolution. We have tested the performance of the algorithm in a 5-zone thermal network, for a certain number of different situations.…”

Section: Related Workmentioning

confidence: 99%

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An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

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An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings

et al. 2020

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“…Moreover, the reductions frequently introduce very significant losses on accuracy. Resistor-Capacitor circuits to model a building have been exploited also in [30], where authors presented a methodology based on Unscented Kalman Filter and thermal network representation to estimate thermal dynamics in buildings. However, the complexity of the model increases for buildings with many rooms, making this approach suitable for small constructions.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings

et al. 2019

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In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for 40 % of total energy consumption only in Europe, affecting more than a third of the total pollution produced. Therefore, energy control policies of buildings (for example, forecast-based policies such as Demand Response and Demand Side Management) play a decisive role in reducing energy waste. On these premises, this paper presents an innovative methodology based on Internet-of-Things (IoT) technology for smart building indoor air-temperature forecasting. In detail, our methodology exploits a specialized Non-linear Autoregressive neural network for short- and medium-term predictions, envisioning two different exploitation: (i) on realistic artificial data and (ii) on real data collected by IoT devices deployed in the building. For this purpose, we designed and optimized four neural models, focusing respectively on three characterizing rooms and on the whole building. Experimental results on both a simulated and a real sensors dataset demonstrate the prediction accuracy and robustness of our proposed models.

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Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems

Gangopadhyay

Tan

Jiang

et al. 2020

Lecture Notes in Computer Science

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A Grey-box Model Based on Unscented Kalman Filter to Estimate Thermal Dynamics in Buildings

Cited by 11 publications

References 19 publications

An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings

An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings

A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings

Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems

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