User Behavior Modeling for Estimating Residential Energy Consumption

Akşanlı, Barış; Akyürek, Alper Sinan; Rosing, Tajana

doi:10.1007/978-3-319-33681-7_29

Cited by 15 publications

(6 citation statements)

References 9 publications

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“…However, their approach is for modelling the activities of the people who work in the industrial sector. In addition to the probability models, Aksanli et al develop a graph-based model to represent the chain of user activities [18], and Basu et al use a decision tree in their modelling [19]. Table 1 summarises the reviewed literature that use TUS data for activity modelling.…”

Section: Related Workmentioning

confidence: 99%

A Stochastic Model for Residential User Activity Simulation

Liu

Yang

et al. 2019

Energies

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User activities is an important input to energy modelling, simulation and performance studies of residential buildings. However, it is often difficult to obtain detailed data on user activities and related energy consumption data. This paper presents a stochastic model based on Markov chain to simulate user activities of the households with one or more family members, and formalizes the simulation processes under different conditions. A data generator is implemented to create fine-grained activity sequences that require only a small sample of time-use survey data as a seed. This paper evaluates the data generator by comparing the generated synthetic data with real data, and comparing other related work. The results show the effectiveness of the proposed modelling approach and the efficiency of generating realistic residential user activities.

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Section: Related Workmentioning

confidence: 99%

A Stochastic Model for Residential User Activity Simulation

Liu

Yang

et al. 2019

Energies

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“…For example, prior research has shown that understanding users behavior and exploiting the behavioral characteristics can be used to improve system efficiency. In this context, earlier work proposed diverse system optimization techniques by identifying user behaviors and interactions for mobile systems [9] and smart homes [1]. Prior work often utilized ML techniques to identify the activities, while relying on computing capability of clouds through offloading, e.g., [18].…”

Section: Related Work Human Activity Recognitionmentioning

confidence: 99%

“…The results are reported for the non-binarized models, since the overhead of the model binarization is negligible. 1 In this comparison, the HD modeling and the neural network training were both executed on x86 processor. The results show that the proposed method presents higher performance efficiency as compared to the neural network training.…”

Section: Efficiency Comparison Training Efficiencymentioning

confidence: 99%

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Efficient human activity recognition using hyperdimensional computing

Kim

Imani

Rosing

2018

Proceedings of the 8th International Conference on the Internet of Things

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Human activity recognition is a key task of many Internet of Things (IoT) applications to understand underlying contexts and react with the environments. Machine learning is widely exploited to identify the activities from sensor measurements, however, they are often overcomplex to run on less-powerful IoT devices. In this paper, we present an alternative approach to efficiently support the activity recognition tasks using brain-inspired hyperdimensional (HD) computing. We show how the HD computing method can be applied to the recognition problem in IoT systems while improving the accuracy and efficiency. In our evaluation conducted for three practical datasets, the proposed design achieves the speedup of the model training by up to 486x as compared to the stateof-the-art neural network training. In addition, our design improves the performance of the HD-based inference procedure by 7x on a low-power ARM processor.

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“…For example, in [17] the authors establish a framework that is able to establish profiles of energy demand in residential areas by means of a mathematical model that details the relationship between human activity and energy consumption. In addition, use an autoregressive moving average model (ARMA) to detect malicious consumption patterns due to electrical intrusions [18].…”

Section: Introductionmentioning

confidence: 99%

Linguistic Modeling and Synthesis of Heterogeneous Energy Consumption Time Series Sets

Martínez-Municio¹,

Rodríguez-Benítez²,

Castillo-Herrera³

et al. 2018

IJCIS

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Thanks to the presence of sensors and the boom in technologies typical of the Internet of things, we can now monitor and record the energy consumption of buildings over time. By effectively analyzing these data to capture consumption patterns, significant reductions in consumption can be achieved and this can contribute to a building's sustainability. In this work, we propose a framework from which we can define models that capture this casuistry, gathering a set of time series of electrical consumption. The objective of these models is to obtain a linguistic summary based on y is P protoforms that describes in natural language the consumption of a given building or group of buildings in a specific time period. The definition of these descriptions has been solved by means of fuzzy linguistic summaries. As a novelty in this field, we propose an extension that is able to capture situations where the membership of the fuzzy sets is not very marked, which obtains an enriched semantics. In addition, to support these models, the development of a software prototype has been carried out and a small applied study of actual consumption data from an educational organization based on the conclusions that can be drawn from the techniques that we have described, demonstrating its capabilities in summarizing consumption situations. Finally, it is intended that this work will be useful to managers of buildings or organizational managers because it will enable them to better understand consumptionin a brief and concise manner, allowing them to save costs derived from energy supply by establishing sustainable policies.

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User Behavior Modeling for Estimating Residential Energy Consumption

Cited by 15 publications

References 9 publications

A Stochastic Model for Residential User Activity Simulation

A Stochastic Model for Residential User Activity Simulation

Efficient human activity recognition using hyperdimensional computing

Linguistic Modeling and Synthesis of Heterogeneous Energy Consumption Time Series Sets

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