Thermal comfort maintenance in demand response programs: A critical review

Fonseca, André Luiz Amorim da; Chvatal, Karin Maria Soares; Fernandes, Ricardo Augusto Souza

doi:10.1016/j.rser.2021.110847

Cited by 34 publications

(8 citation statements)

References 128 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the last stage of the proposed methodology, the feature vector extracted by the DTLFE was presented to the following classifiers: MLP, SVM and XGBOOST. In order to assess the performance of the DTLFE in conjunction with the classifiers, two other comparative approaches were also considered: (1) based on the extraction of features by means of recurrence quantification analysis (RQA), using the indicators called determinism and recurrence rate as inputs to the three same classifiers; and (2) based on the use of RPs as inputs to a CNN classifier, such as proposed in [34]. A block diagram representing the comparison test is presented in Fig.…”

Section: Classification Using Machine Learningmentioning

confidence: 99%

“…Although the technological evolution has allowed the development/improvement of electrical devices, there is an increase in electricity consumption. In this sense, the identification of consumers' electricity usage profile becomes a valuable task, mainly to demand response programs [1]. To this end, nonintrusive load monitoring (NILM) emerged as a research area that effectively contributes to this identification, mainly for the residential sector [2]- [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Transfer Learning-Based Feature Extraction: An Approach to Improve Nonintrusive Load Monitoring

Cavalca

Fernandes

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

The development of techniques that allow the efficient identification of residential loads (nonintrusive load monitoring) is a key factor for the practical implementation of demand response programs. Recently, in terms of nonintrusive load monitoring, the use of deep learning has gained attention, mainly the models based on convolutional neural networks. However, the efficient training of these models is strongly dependent on the quantity and balance of the data, i.e., characteristics that are not normally found in nonintrusive load monitoring datasets. To deal with these challenges, this paper proposes an approach based on three stages, that are: (i) time series transformation into 2D images; (ii) feature extraction using deep transfer learning; and (iii) classification/labelling of loads. Moreover, it was analyzed and defined the better window size per load in relation to the f1-score reached by the classifiers. In this sense, it was considered five loads present in the Reference Energy Disaggregation Dataset, where the proposed approach was able to obtain an average f1-score of 83.2%. From the results analysis, it was demonstrated the greater capacity of the proposed approach to infer and generalize its responses. INDEX TERMSConvolutional neural network; deep transfer learning; feature extraction; nonintrusive load monitoring; recurrence plots.

show abstract

Section: Classification Using Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning-Based Feature Extraction: An Approach to Improve Nonintrusive Load Monitoring

Cavalca

Fernandes

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…DR programs can be divided into two main categories: priceand incentive-based programs (Asadinejad et al, 2018;Fonseca et al, 2021). The price-based programs provide customers with different prices at various periods, such as time of use (TOU) pricing, real-time pricing (RTP), and inclining block rate (IBR) tariffs (Siano, 2014;Deng et al, 2015).…”

Section: Introduction 1motivationmentioning

confidence: 99%

Analysis of social–Psychological factors and financial incentives in demand response and residential energy behavior

Chen

Cao

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

Introduction: Demand response programs are promising approaches to balance supply and demand and reduce or shift peak hour loads. However, the impacts of residents’ behavioral patterns and social–psychological factors on these programs are poorly understood.Method: We used an extended model of the theory of planned behavior to understand how social–psychological factors, demographics, and household characteristics influence the demand response behaviors (i.e., adjusting A/C and shifting the time of use) of 826 US residents who participated in our study.Results: We found that low-income households (43.7%) reported as more likely to respond to the behavior of raising A/C thermostat settings when no one is home compared to higher-income residents. Thermal comfort needs (β = 0.34) and participants’ age (β = 0.02) increased the financial incentives requested to accept higher A/C temperatures while someone is home. Thermal comfort needs (β = 0.36) and energy-saving habits (β = −0.11) are the significant predictors of increased incentives requested for higher A/C temperatures when no one is home. Older adults request higher financial incentives for shifting washer and dryer usage than younger residents. Finally, attitudes toward energy saving are the strongest predictor of electricity curtailment behavioral intention, followed by perceived behavioral control, subjective norms, cost and environmental concerns, and energy-saving habits.Discussion: This finding suggests the potential of social–psychological variables in shaping energy consumption behaviors. This study provides insights for designing effective demand response programs and broadly analyzing energy behavioral patterns.

show abstract

“…Storing the excess as thermal energy is another way to improve the self-consumption of the system. Provided that a heat pump-based thermal installation is already present, no Energies 2023, 16, 5156 2 of 27 extra elements have to be added; the water tanks and the thermal inertia of the building can be used actively [3].…”

Section: Introductionmentioning

confidence: 99%

“…The question of controlling and optimizing the integration between heat pumps and PV panels has been quite extensively analyzed in the literature [3,[7][8][9][10][11][12][13][14]. The studies can be grouped into two different solutions: rule-based controls and optimization-based controls.…”

Section: Introductionmentioning

confidence: 99%

Energy and Cost Analysis of an Integrated Photovoltaic and Heat Pump Domestic System Considering Heating and Cooling Demands

Arenas-Larrañaga,

Santos-Mugica,

Alonso-Ojanguren

et al. 2023

Energies

View full text Add to dashboard Cite

The integration of photovoltaic panels and heat pumps in domestic environments is a topic that has been studied extensively. Due to their electrical nature and the presence of elements that add thermal inertia to the system (water tanks and the building itself), the functioning of compression heat pumps can be manipulated to try to fulfill a certain objective. In this paper, following a rule-based control concept that has been identified in commercial solutions and whose objective is to improve the self-consumption of the system by actively modulating the heat pump compressor, a parametric analysis is presented. By making use of a lab-tested model, the performance of the implemented control algorithm is analyzed. The main objective of this analysis is to identify and quantify the effects of the main parameters in the performance of the system, namely the climate (conditioning both heating and cooling demands), the photovoltaic installation size, the thermal insulation of the building and the control activation criteria. A total of 168 yearly simulations have been carried out. The results show that the average improvement in self-consumption is around 13%, while the cost is reduced by 2.5%. On the other hand, the heat from the heat pump and the power consumed increase by 3.7% and 5.2%, respectively. Finally, a linear equation to estimate the performance of the controller is proposed.

show abstract

Thermal comfort maintenance in demand response programs: A critical review

Cited by 34 publications

References 128 publications

Deep Transfer Learning-Based Feature Extraction: An Approach to Improve Nonintrusive Load Monitoring

Deep Transfer Learning-Based Feature Extraction: An Approach to Improve Nonintrusive Load Monitoring

Analysis of social–Psychological factors and financial incentives in demand response and residential energy behavior

Energy and Cost Analysis of an Integrated Photovoltaic and Heat Pump Domestic System Considering Heating and Cooling Demands

Contact Info

Product

Resources

About