Development of the heating load prediction model for the residential building of district heating based on model calibration

Zhang, Qiang; Tian, Zhe; Ma, Zhijun; Li, Genyan; Lu, Yakai; Niu, Jide

doi:10.1016/j.energy.2020.117949

Cited by 45 publications

(11 citation statements)

References 32 publications

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“…Heat usage patterns prediction is the biggest challenge in order to facilitate an effective model predictive-based DH network. It becomes possible to optimize the overall heat production, lower grid losses, and enhance the energy usage efficiency with an accurate heat usage patterns prediction model 9 . Another practical factor that raises the requirement for heat usage forecasting is that the heat supplied to the customers must match their real-time demand in order to ensure the distribution temperature is in an acceptable range 10 .…”

Section: Introductionmentioning

confidence: 99%

Daily and seasonal heat usage patterns analysis in heat networks

Dang

et al. 2022

Sci Rep

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Heat usage patterns, which are greatly affected by the users' behaviors, network performances, and control logic, are a crucial indicator of the effective and efficient management of district heating networks. The variations in the heat load can be daily or seasonal. The daily variations are primarily influenced by the customers' social behaviors, whereas the seasonal variations are mainly caused by the large temperature differences between the seasons over the year. Irregular heat load patterns can significantly raise costs due to pricey peak fuels and increased peak heat load capacities. The in-depth analyses of heat load profiles are regrettably quite rare and small-scale up until now. Therefore, this study offers a comprehensive investigation of a district heating network operation in order to exploit the major features of the heat usage patterns and discover the big factors that affect the heat load patterns. In addition, this study also provides detailed explanations of the features that can be considered the main drivers of the users' heat load demand. Finally, two primary daily heat usage patterns are extracted, which are exploited to efficiently train the prediction model.

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Section: Introductionmentioning

confidence: 99%

Daily and seasonal heat usage patterns analysis in heat networks

Dang

et al. 2022

Sci Rep

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“…Hanmin Caia et al [12] develop a distributed demand response method based on exchange ADMM, which relieves network congestion and helps reduce the use of primary energy. Qiang Zhang, Zhe Tian, et al [13] propose the demand-side method, which predicts the heating load of terminal buildings considering the influence of indoor temperature. Ehsan Kamel and et al [14] select the most impactful inputs to choose the type and quantity of sensors for deployment that improve the data-driven models' accuracy and minimize the costs.…”

Section: Aiim-2021mentioning

confidence: 99%

Heat Supply Optimization Based On Machine Learning And Knowledge Graph

Xie

2022

J. Phys.: Conf. Ser.

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At present, heating companies mostly use a set-point temperature control curve. The operator adjusts the temperature control curve according to watching the outdoor weather conditions, which usually results in economic conditions. Given the above condition, we analyze the heating system’s mechanism first, then compile and analyze much historical data. According to the analysis results, we establish the heat exchange station-user room temperature joint model based on the machine learning method. Using the above model, the system is deeply explored, and the optimized secondary heating curve is designed and simulated. The results show that the user’s room temperature stability has an obvious control effect, which is of great significance to meet the room temperature compliance rate. At the same time, the knowledge graph of the heating field and some applications based on the knowledge graph are also built to provide more convenient and friendly operating advice for operators.

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“…HL refers to the entire quantity of heat energy necessary to maintain a standard room temperature [7]. The HL prediction can assist heating operators in predicting heat demand and developing realistic operation plans [8]. At present, there are several research studies on the prediction of HL.…”

Section: Introductionmentioning

confidence: 99%

Heating Load of Residential Buildings Using Multiple Linear Regression Artificial Neural Network

Ibrahim

Kamaruddin

Nasir

2022

ARFMTS

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Global warming is one metric of climate change which is defined as an increase in the average global temperature. Residential buildings contribute significantly to the pollution that causes climate change. It is essential to have a comprehensive understanding of the functions of highly energy efficient buildings in view of projected climate projections, the quality of their heating systems, and the impact on human health and well-being. Thus, in this study, the effects of six input variables which are Overall Height, Glazing Area, Wall Area, Relative Compactness, Roof Area, and Glazing Area Distribution on one output variable, namely Heating Load (HL) of residential buildings was investigated using Multiple Linear Regression and Artificial Neural Network (MLR-ANN) approaches. Two-layer hyperbolic tangent-identity transfer functions with 6-3-1 configurations were employed as it was found as the best neural network model. A dataset of 768 residential buildings was used for secondary data. The Mean Square Error (MSE), determination coefficients R2, as well as the percentage of normalized importance analysis were used to assess the statistical prediction capabilities of the MLR-ANN model. Based on the current findings, Wall Area is the most contributing factor towards HL, followed by Relative Compactness, Roof Area, Overall Height, Glazing Area, and Glazing Area Distribution. It can be suggested that HVAC (heating, ventilation, and air conditioning) systems should be implemented in residential buildings to reduce energy use. Natural ventilation is encouraged in buildings through vernacular design, and radiant heating and cooling systems as it is very effective and efficient way of providing thermal comfort within a structure.

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Development of the heating load prediction model for the residential building of district heating based on model calibration

Cited by 45 publications

References 32 publications

Daily and seasonal heat usage patterns analysis in heat networks

Daily and seasonal heat usage patterns analysis in heat networks

Heat Supply Optimization Based On Machine Learning And Knowledge Graph

Heating Load of Residential Buildings Using Multiple Linear Regression Artificial Neural Network

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