Forecasting the Heat Load of Residential Buildings with Heat Metering Based on CEEMDAN-SVR

Abstract

The energy demand of the district heating system (DHS) occupies an important part in urban energy consumption, which has a great impact on the energy security and environmental protection of a city. With the gradual improvement of people’s economic conditions, different groups of people now have different demands for thermal energy for their comfort. Hence, heat metering has emerged as an imperative for billing purposes and sustainable management of energy consumption. Therefore, forecasting the heat load of buildings with heat metering on the demand side is an important management strategy for DHSs to meet end-users’ needs and maintain energy-saving regulations and safe operation. However, the non-linear and non-stationary characteristics of buildings’ heat load make it difficult to predict consumption patterns accurately, thereby limiting the capacity of the DHS to deliver on its statutory functions satisfactorily. A novel ensemble prediction model is proposed to resolve this problem, which integrates the advantages of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and support vector regression (SVR), called CEEMDAN-SVR in this paper. The proposed CEEMDAN-SVR algorithm is designed to automatically decompose the intrinsic mode according to the characteristics of heat load data to ensure an accurate representation of heat load patterns on multiple time scales. It will also be useful for developing an accurate prediction model for the buildings’ heat load. In formulating the CEEMDAN-SVR model, the heat load data of three different buildings in Xingtai City were acquired during the heating season of 2019–2020 and employed to conduct detailed comparative analysis with modern algorithms, such as extreme tree regression (ETR), forest tree regression (FTR), gradient boosting regression (GBR), support vector regression (SVR, with linear, poly, radial basis function (RBF) kernel), multi-layer perception (MLP) and EMD-SVR. Experimental results reveal that the performance of the proposed CEEMDAN-SVR model is better than the existing modern algorithms and it is, therefore, more suitable for modeling heat load forecasting.

Using Machine Learning to Identify the Potential Marginal Land Suitable for Giant Silvergrass (Miscanthus × giganteus)

“…A certain amount of sample data is randomly selected several times during the operation to analyze the influence of the independent variable on the dependent variable, while the remaining sample data are used to test the fitting results, and the averages of the generated multiple regressions are the final output [39,40]. The BRT method can yield the influence of the independent variable on the dependent variable and the interrelationship between that independent variable and the dependent variable when the other independent variables are taken as the mean or constant [41]. We believe it was a wise choice to select the BRT model for this study due to its success in the application of simulating species distribution and identifying marginal land for energy plants, such as sweet sorghum [28] and cassava [29].…”

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

“…Deep learning enhances the understanding of spatial features and aids in various environmental applications, such as land cover mapping, change analysis, evapotranspiration, solar radiation evaluation, and natural hazard prediction, by replacing labor-intensive manual image analysis. These methods are designed for heightened prediction accuracy, heralding a paradigm shift in leveraging datasets for environmental analysis (Gao et al 2020;Lehnert et al 2021). Deep-learning techniques in the field of environmental remote sensing undoubtedly proved one of the most important breakthroughs, offering solutions to several problems (Baniya et al 2018;Tzampoglou and Loupasakis 2017).…”

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

“…Considering the most important elements is critical in developing an accurate HL forecast model [24]. Many external factors have an impact on district HL.…”