Analysis of Electric Heating Load Characteristics in South Hebei Power Grid

Han, Jinglin; Ma, Guozhen; Hu, Shivao; Shi, Pengjia; J, An; Yang, Yang; Shao, Hua; Zhai, Guofu; Xin, Qi

doi:10.1109/ei2.2018.8582155

Cited by 5 publications

(2 citation statements)

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“…Formulas (7) and (8) show that hi and gi determine the value of the objective function of prediction error. Therefore, EGBT can set various loss functions according to the demand predicted by the actual heat supply project.…”

Section: Single Model Designmentioning

confidence: 99%

“…Heat load accounts for a large proportion of building energy consumption, making it imperative to save heating energy [1][2][3][4][5][6][7][8]. The accurate prediction of the short-term heat load trend of buildings helps to avoid energy waste, and provides a promising way to precisely regulate building energy based on energy demand [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Application of Optimal Combinatory Mathematics Model in Heat Load Trend Prediction

Liu¹,

Liu²,

Jiang³

et al. 2022

IJHT

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The accurate prediction of the short-term heat load trend of buildings helps to prevent energy waste. Combinatory prediction methods need to be studied, as the processing of real-time data series of heat supply and the accurate prediction of short-term trend are more and more demanded by different heating districts and independent buildings. Therefore, this study explores the application of optimal combinatory mathematics model in heat load trend prediction. Firstly, feature extraction was performed on the historical weather data at the locality of the building and the historical data on heat load, creating a short-term trend prediction dataset with days as the unit, and an ultra-short-term trend prediction dataset with hours as the unit. On this basis, a combinatory mathematics model was created for heat load trend prediction. Furthermore, the authors detailed the principles of the two methods, namely, extreme gradient boosting tree (EGBT) and support vector regression (SVR), and explains the combination pattern of the single models in the combinatory model. Then, the weights were optimized by the simulated annealing (SA) algorithm, and the steps of the combinatory model were presented. The proposed model was proved effective through experiments.

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Section: Single Model Designmentioning

confidence: 99%