Heat load forecasting model considering two dimensional changes of time series DOI
Quanwei Tan,

Guijun Xue,

Wenju Xie

и другие.

Building Services Engineering Research and Technology, Год журнала: 2024, Номер 45(6), С. 775 - 794

Опубликована: Авг. 18, 2024

The regulation strategy of a district heating system is adjusted based on accurate heat load prediction, which not only effectively reduces energy consumption but also improves efficiency and user comfort. In order to improve the accuracy forecasting, forecasting model considering two-dimensional change time series introduced in this paper. Firstly, original data denoised by SVMD decomposition, several stationary regular modal components are obtained. Then, three strategies were used enhance BWO algorithm, IBWO-TimesNet prediction was established extract hidden information from perspective. Finally, performance evaluated detail through case analysis. results show that MAE, RMSE R2 SVMD-IBWO-TimesNet 0.647, 1.190 99.1%, respectively. Compared with other mainstream models, has higher accuracy. addition, even if training samples reduced, can still predict strong generalization ability. Therefore, verified, provides reference for control load. Practical application Heat vital task particularly relation its impact management building efficiency. contribution paper provide advanced algorithms This insight derived modelling will assist professionals pursuit more needs buildings, thereby optimizing design operation systems. practical technology could save costs, reduce carbon emissions, comfort sustainability buildings.

Язык: Английский

Time series analysis in compressor-based machines: a survey DOI Creative Commons
Francesca Forbicini, Nicolò Oreste Pinciroli Vago, Piero Fraternali

и другие.

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Март 5, 2025

Язык: Английский

Процитировано

0
Energy Consumption Prediction for Water-Based Thermal Energy Storage Systems Using an Attention-Based TCN-LSTM Model DOI

Jianjie Cheng,

Shiyu Jin,

Zehao Zheng

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106383 - 106383

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Hybrid forecasting model for Central Air Conditioning Load Based on CEEMDAN and WTCN-GRU DOI
Yang Guo, Ming Chen,

Hong Wang

и другие.

International Journal of Refrigeration, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
HALO: HVAC Load Forecasting With Industrial IoT and Local-Global-Scale Transformer DOI
Cheng Pan, Cong Zhang, Edith C.‐H. Ngai

и другие.

IEEE Internet of Things Journal, Год журнала: 2024, Номер 11(17), С. 28307 - 28319

Опубликована: Май 15, 2024

The evolution of Internet-of-Things (IoT) is fostering the use intelligent controls for energy conservation. Yet, efficacy these strategies largely tied to diverse load forecasting algorithms. Given significant contribution heating, ventilation, and air-conditioning (HVAC) systems global consumption, accurate HVAC power usage crucial improving overall efficiency. However, real-world forecasting, bolstered by various IoT devices, complicated multiple factors: data variability, fluctuations, electronic phenomena (e.g., zero drifts), increased time complexity larger model sizes required manage accumulating historical data. To address challenges, we first present an in-depth measurement study on characteristics at a minute scale based collected in six locations. We propose HALO, transformer-based framework specifically designed load. HALO incorporates adaptive pre-processing stage local-global-scale stage, enabling precise optimization utilization. Evaluation traces from prototype application demonstrates that proposed significantly outperforms existing models.

Язык: Английский

Процитировано

2
A novel trend and periodic characteristics enhanced decoupling framework for multi-energy load prediction of regional integrated energy systems DOI

Wei Zhuang,

Qingyu Xi,

Chenxi Lu

и другие.

Electric Power Systems Research, Год журнала: 2024, Номер 237, С. 111028 - 111028

Опубликована: Сен. 2, 2024

Язык: Английский

Процитировано

2
Building electricity load forecasting based on spatiotemporal correlation and electricity consumption behavior information DOI

Xianzhou Dong,

Yongqiang Luo, Shuo Yuan

и другие.

Applied Energy, Год журнала: 2024, Номер 377, С. 124580 - 124580

Опубликована: Сен. 26, 2024

Язык: Английский

Процитировано

2
Research on the day-ahead scheduling optimization method of medium-depth geothermal cascade heating system DOI

Shilei Lu,

Caihong Li, Ran Wang

и другие.

Journal of Building Engineering, Год журнала: 2023, Номер 82, С. 108184 - 108184

Опубликована: Ноя. 29, 2023

Язык: Английский

Процитировано

5
Prophet–CEEMDAN–ARBiLSTM-Based Model for Short-Term Load Forecasting DOI Creative Commons

Jindong Yang,

Xiran Zhang,

Wenhao Chen

и другие.

Future Internet, Год журнала: 2024, Номер 16(6), С. 192 - 192

Опубликована: Май 31, 2024

Accurate short-term load forecasting (STLF) plays an essential role in sustainable energy development. Specifically, companies can efficiently plan and manage their generation capacity, lessening resource wastage promoting the overall efficiency of power utilization. However, existing models cannot accurately capture nonlinear features electricity data, leading to a decline performance. To relieve this issue, paper designs innovative method, named Prophet–CEEMDAN–ARBiLSTM, which consists Prophet, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), residual Bidirectional Long Short-Term Memory (BiLSTM) network. firstly employs Prophet method learn cyclic trend from input aiming discern influence these on load. Then, adopts CEEMDAN decompose series yield components distinct modalities. In end, advanced BiLSTM (ARBiLSTM) block as above extracted obtain results. By conducting multiple experiments New England public dataset, it demonstrates that Prophet–CEEMDAN–ARBiLSTM achieve better performance compared Prophet-based ones.

Язык: Английский

Процитировано

1
Mitigation imbalance distribution: Data augmentation of local small sample for building electricity load in time-series generative adversarial network DOI
Shengdong Zhang, Jiangjiang Wang, Zhiqiang Yin

и другие.

Journal of Building Engineering, Год журнала: 2024, Номер 99, С. 111549 - 111549

Опубликована: Дек. 9, 2024

Язык: Английский

Процитировано

1
Temporal Convolutional Neural Network-Based Cold Load Prediction for Large Office Buildings DOI

Zengxi Feng,

Lutong Zhang,

Wenjing Wang

и другие.

Journal of Thermal Science and Engineering Applications, Год журнала: 2024, Номер 16(11)

Опубликована: Сен. 5, 2024

Abstract In heating, ventilation, and air conditioning (HVAC) systems for large office buildings, accurate cooling load prediction facilitates the elaboration of energy-efficient energy-saving operation strategies system. this paper, a hybrid model based on gray relational analysis-improved black widow optimization algorithm-temporal convolutional neural network (GRA-IBWOA-TCN) is proposed cold buildings. First, factors influencing in buildings were analyzed, with GRA used to identify key features reduce input data dimensionality model. Second, three improvement are enhance performance at different stages algorithm, aimed establishing optimizing TCN hyper-parameters through IBWOA. Finally, algorithm comparison experiments conducted intra-week dataset (T1) weekend (T2) building as study samples, respectively. The results show that mean absolute percentage error values GRA-IBWOA-TCN T1 T2 datasets 0.581% 0.348%, respectively, which 81.1% 88.3% lower compared model, exhibit highest accuracy models, such backpropagation, support vector machine, long short-term memory, network, multiple algorithms, good stability, generalization ability. summary, paper can provide effective technical management HVAC

Язык: Английский

Процитировано

0