A methodology to estimate space heating and domestic hot water energy demand profile in residential buildings from low-resolution heat meter data

Pereira, Daniel; Johra, Hicham; Marszal-Pomianowska, Anna; Pomianowski, Michal Zbigniew

doi:10.1016/j.energy.2022.125705

Cited by 17 publications

(8 citation statements)

References 18 publications

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“…The error distribution is quite diverse, with five apartments showing an EWBs demand overestimation of more than +25%. Among these, one household had an extreme EWBs prediction with an overestimation of +85%, while four apartments had a slight underestimation of over −10% [34].…”

Section: Statistical Evaluation Approachmentioning

confidence: 97%

“…Data from numerous sources each contributes unique insights into various aspects of energy consumption behaviour, which are generally obtained from the electrical water boiler system specification, which normally contains its efficiency rating, capacity, and heating element specifications, among other things [120]. Regardless, data collection and measurement techniques must be methodical, rational, and standardised [2,34]. Table 4 shows the source of data and measures as found in the literature; these are sometimes obtained from the manufacturer's specification or from actual measurements collected during systems operation.…”

Section: Data and Measurement Techniques For The Prediction Of Electr...mentioning

confidence: 99%

“…Also, the squaring operation eliminates the sign of the error, which limits RMSE in providing information about the direction of errors (overestimation or underestimation). It treats all deviations equally without distinguishing between over-predictions and under-predictions [34]. In cases where the distribution of errors is not normal, alternative evaluation metrics might be more appropriate;…”

Section: Statistical Evaluation Approachmentioning

confidence: 99%

“…Their study subsequently delved into assessing the performance of evaluation metrics, aiming to estimate the accuracy and suitability of the prediction models. Subsequently, Leiria et al introduced a data-driven methodology to estimate hot water boiler prediction based on hourly measurements, where hybrid support vector regression (SVR) and Kalman filters yielded a better result but still required further investigation of other estimation methods of prediction [34].…”

Section: Introductionmentioning

confidence: 99%

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Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions

Kachalla,

Ghiaus

2024

Energies

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Accurate and efficient prediction of electric water boiler (EWB) energy consumption is significant for energy management, effective demand response, cost minimisation, and robust control strategies. Adequate tracking and prediction of user behaviour can enhance renewable energy mini-grid (REMD) management. Fulfilling these demands for predicting the energy consumption of electric water boilers (EWB) would facilitate the establishment of a new framework that can enhance precise predictions of energy consumption trends for energy efficiency and demand management, which necessitates this state-of-the-art review. This article first reviews the factors influencing the prediction of energy consumption of electric water boilers (EWB); subsequently, it conducts a critical review of the current approaches and methods for predicting electric water boiler (EWB) energy consumption for residential building applications; after that, the performance evaluation methods are discussed. Finally, research gaps are ascertained, and recommendations for future work are summarised.

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Section: Statistical Evaluation Approachmentioning

confidence: 97%

Section: Data and Measurement Techniques For The Prediction Of Electr...mentioning

confidence: 99%

Section: Statistical Evaluation Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions

Kachalla,

Ghiaus

2024

Energies

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“…Smart meters and algorithm control could be effective in small-scale DH systems [28][29][30]. An algorithm, coupling downsized heat pumps to radiant emitters, based on thermal inertia control was studied and the seasonal performance of heat pumps increased by 10% [31].…”

Section: Influence Of User Regulation On Heat Energy Consumptionmentioning

confidence: 99%

Thermal Characteristics and User Regulation of Household Heat Metering for Residential District Heating Systems in Northern China

Luo,

Li,

Xia

et al. 2024

Energies

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Heat metering is an important measure of China’s heating marketization reform as well as an energy-saving policy implemented in buildings nearly 20 years ago. However, this policy has not achieved the expected results due to various reasons. It is important to note that although northern China is dominated by large apartment buildings, resulting in unique thermal characteristics and user behavior, the measured results of these characteristics are not common. In this study, data from cities in northern China were obtained using household heat meters and field testing. Based on the three levels of station, building, and household, the effects of location, heat outages, and users’ regulation behavior on energy consumption were analyzed. The results show that different locations lead to a considerable difference in heat consumption among users, and an outage leads to a significant increase in consumption by surrounding users. For typical buildings in northern China, it is challenging to realize energy savings with district heating systems based on user behavior, and household metering and charging are not fair and reliable. Adjusting the heat meter unit from the household to the building was suggested.

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The Accuracy of Gas Demand Disaggregation into Space Heating, Domestic Hot Water, and Cooking Demands Using Diverse Models and Datasets

Willems,

Smeets,

Saelens

2024

Lecture Notes in Civil Engineering

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A methodology to estimate space heating and domestic hot water energy demand profile in residential buildings from low-resolution heat meter data

Cited by 17 publications

References 18 publications

Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions

Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions

Thermal Characteristics and User Regulation of Household Heat Metering for Residential District Heating Systems in Northern China

The Accuracy of Gas Demand Disaggregation into Space Heating, Domestic Hot Water, and Cooking Demands Using Diverse Models and Datasets

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