Methods for a Smart Thermostat to Estimate the Characteristics of a House Based on Sensor Data

Ham, Wim van der; Klein, Michel; Tabatabaei, Seyed Amin; Thilakarathne, Dilhan J.; Treur, Jan

doi:10.1016/j.egypro.2016.09.067

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…Table I shows the details of the simulated house in normal situation. It is assumed that the installed thermostat measures the inside and outside temperatures per minute, and the energy provided to the system is measured per hour (similar to the real dataset that is used in [6]). Fig.…”

Section: Simulated Modelmentioning

confidence: 99%

“…In [6], a simple thermodynamic model of a house is introduced on the basis of capacity C and the loss rate ε of a house. In this section, an adapted version of this method is presented, and it is explained how to estimate the value of C and ε according to the collected data.…”

Section: Estimating Thermal Properties Of the Housementioning

confidence: 99%

“…In [7], it is suggested to use the historical data of a thermostat to compare the usage of a house in two different time periods, with almost the same outdoor temperature. The approach proposed in [6] does not have such a constraint, and can be used for temporal comparison between any two periods with different outdoor temperature.…”

Section: Introductionmentioning

confidence: 99%

“…To do that, it is better to compare each house to houses with comparable thermal properties. The authors of [6] proposed a technique to estimate the characteristics of the house, by analyzing the data collected by a thermostat. These estimations can be used to cluster the buildings according to their characteristics, and compare the consumption of each house with similar ones.…”

Section: Introductionmentioning

confidence: 99%

“…To achieve that, the data collected by a relatively simple thermostat are used to estimate the thermodynamic characteristics of the building (like in [6]), and by performing temporal comparison for these properties, both the existence of a problem and also its cause are diagnosed.…”

Section: Introductionmentioning

confidence: 99%

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A Data Analysis Approach for Diagnosing Malfunctioning in Domestic Space Heating

Tabatabaei¹

2016

Proceedings of ICT for Sustainability 2016

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Abstract-Around one third of worldwide energy usage is for the residential section and 60% of the energy consumption in this domestic area is for space heating. Therefore, monitoring and controlling this part of energy usage can have a major effect on the overall energy consumption and also on the emission of green house gases. Smart thermostats can play an important role in achieving more economic energy usage in the residential section. This paper shows an innovative way to use data collected by a relatively simple thermostat to estimate the thermal properties of a building. Moreover, it will be shown that in case of malfunctioning and high usage, this method can be used to firstly find out that there is a problem, and also to identify the cause of the problem. This information can be used as feedback to the resident to help him or her to discover the problem, and also point at the cause of it. Applying this idea in a smart thermostat or a mobile application with access to the data gathered by a simple thermostat can encourage residents to limit their energy usage for space heating. And in case of a problem that leads to an increase in energy usage, it can understand its occurrence and diagnose the cause.

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Section: Simulated Modelmentioning

confidence: 99%

Section: Estimating Thermal Properties Of the Housementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Data Analysis Approach for Diagnosing Malfunctioning in Domestic Space Heating

Tabatabaei¹

2016

Proceedings of ICT for Sustainability 2016

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Nonparametric user activity modelling and prediction

Bock

Auquilla

Nowé

et al. 2020

User Model User-Adap Inter

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Modelling the occupancy of buildings, rooms or the usage of machines has many applications in varying elds, exemplied by the fairly recent emergence of smart, self-learning thermostats. Typically, the aim of such systems is to provide insight into user behaviour and incentivise energy savings or to automatically reduce consumption while maintaining user comfort. This paper presents a non-parametric user activity modelling algorithm, i.e. a Dirichlet process mixture (DPM) model, implemented by Gibbs sampling and the stick-breaking process, to infer the underlying patterns in user behaviour from the data. The technique deals with multiple activities, such as , of multiple users. Furthermore, it can also be used for modelling and predicting appliance usage (e.g. ).The algorithm is evaluated, both on cluster validity and predictive performance, using three case studies of varying complexity. The obtained results indicate that the method is able to properly assign the activity data into welldened clusters. Moreover, the high prediction accuracy demonstrates that these clusters can be exploited to anticipate future behaviour, facilitating the development of intelligent building management systems.

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Predictive maintenance for residential air conditioning systems with smart thermostat data using modified Mann-Kendall tests

Guo

Rasmussen

2023

Applied Thermal Engineering

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Methods for a Smart Thermostat to Estimate the Characteristics of a House Based on Sensor Data

Cited by 16 publications

References 8 publications

A Data Analysis Approach for Diagnosing Malfunctioning in Domestic Space Heating

A Data Analysis Approach for Diagnosing Malfunctioning in Domestic Space Heating

Nonparametric user activity modelling and prediction

Predictive maintenance for residential air conditioning systems with smart thermostat data using modified Mann-Kendall tests

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