Daily residential heat load prediction based on a hybrid model of signal processing, econometric model, and support vector regression

Xue, Guixiang; Zhang, Yahui; Yu, Shi-ang; Song, Jiancai; Bian, Tianjian; Yuan, Gao; Yan, Wenjie; Guo, Yang

doi:10.1016/j.tsep.2023.102005

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…With the rapid advancement of industrial hardware and artificial intelligence technology, models based on machine learning and deep learning have demonstrated significant capabilities for learning and mapping nonlinear objects, offering new potential for the development of heat load prediction. Examples of popular machine learning regression techniques include support vector regression (SVR) [13], decision tree (DT) [14], random forest (RF) [15], and neural network models [16]. Li et al [17] developed a heat load prediction model for heating systems using a BP neural network by quantifying temperature and date types.…”

Section: Related Workmentioning

confidence: 99%

A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model

Yang,

Yan,

Zhou

2024

Applied Sciences

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Accurate prediction of the heat load in district heating systems is challenging due to various influencing factors, substantial transmission lag in the pipe network, frequent fluctuations, and significant peak-to-valley differences. An autoencoder—grey wolf optimization—gated recurrent unit (AE-GWO-GRU)-based heat load prediction method for district heating systems is proposed, employing techniques such as data augmentation, lag feature extraction, and input feature extraction, which contribute to improvements in the model’s prediction accuracy and heat load control stability. By using the AE approach to augment the data, the issue of the training model’s accuracy being compromised due to a shortage of data is effectively resolved. The study discusses the influencing factors and lag time of heat load, applies the partial autocorrelation function (PACF) principle to downsample the sequence, reduces the interference of lag and instantaneous changes, and improves the stationary characteristics of the heat load time series. To increase prediction accuracy, the GWO algorithm is used to tune the parameters of the GRU prediction model. The prediction error, measured by RMSE and MAPE, dropped from 56.69 and 2.45% to 47.90 and 2.17%, respectively, compared to the single GRU prediction approach. The findings demonstrate greater accuracy and stability in heat load prediction, underscoring the practical value of the proposed method.

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Section: Related Workmentioning

confidence: 99%

A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model

Yang,

Yan,

Zhou

2024

Applied Sciences

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“…One of them is the creation of separate technological components for modelling the production process and the model that covers the trading of standardized energy products in energy markets [7]. The solution of the technical part is closely related to the ability to predict the heat consumption load for the next period [8] and [9]. Commonly available products such as Keras [10], xgboost [11] are used for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Management of Production Processes in a Heating Company

Garbier,

Corejova

2024

Preprint

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The paper is focused on researching the behavior of heating companies in connection with current developments in the electricity market and flexibility in the context of market behavior. The work assesses the increase in profitability through the creation of a technical-economic model using an objective function with profit maximization. The paper objective is presented the procedure and methodology for creating a model using the basic scheme of production processes integrated into the system platform. The result of the work is a comparative analysis of modelled cases of implemented operation deployment according to a defined period and modelling modes in selected time series. The description of individual outputs demonstrates the economic advantage of using combinations of modes of combined electricity and heat production, and non-combined electricity and heat production, including the use of the heat suppression mode because of overproduction of electricity.

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Discussion on the application of sequential turbocharging technology on trawlers

Li,

Shi

2024

Proceedings of the Institution of Mechanical Engineers, Part E:

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To improve the performance needs of trawlers under the varying load and speed conditions of free-running and trawling operations, this article employs sequential turbocharging technology. By fitting the experimental curves of basic turbocharging and conventional turbocharging (two turbochargers), we obtained the universal characteristic curve of sequential turbocharging. Further, the propeller characteristic curves and load characteristic curves of trawlers under free-running and trawling operations were integrated. The analysis results show that the sequential turbocharging system increases the torque of trawlers by about 20% in the low-speed range; switching to two turbochargers in free-running mode reduces fuel consumption by ∼17.5 g/Wh; switching to basic turbocharging during trawling operations decreases fuel consumption by about 13.9 g/Wh. This study is not only beneficial for the economy of ships but also provides guidance for further improvements in engine mechanics, potentially attracting the interest of readers in the main engine manufacturing field.

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Daily residential heat load prediction based on a hybrid model of signal processing, econometric model, and support vector regression

Cited by 5 publications

References 48 publications

A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model

A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model

Management of Production Processes in a Heating Company

Discussion on the application of sequential turbocharging technology on trawlers

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