Comparison of machine-learning models for predicting short-term building heating load using operational parameters DOI
Yong Zhou, Yanfeng Liu, Dengjia Wang

et al.

Energy and Buildings, Journal Year: 2021, Volume and Issue: 253, P. 111505 - 111505

Published: Sept. 23, 2021

Language: Английский

District heater load forecasting based on machine learning and parallel CNN-LSTM attention DOI Creative Commons

Won Hee Chung,

Yeong Hyeon Gu, Seong Joon Yoo

et al.

Energy, Journal Year: 2022, Volume and Issue: 246, P. 123350 - 123350

Published: Feb. 1, 2022

Accurate heat load forecast is important to operate combined and power (CHP) efficiently. This paper proposes a parallel convolutional neural network (CNN) - long short-term memory (LSTM) attention (PCLA) model that extracts spatiotemporal characteristics then intensively learns importance. PCLA by derived spatial temporal features parallelly from CNNs LSTMs. The novelty of this lies in the following three aspects: 1) for forecasting proposed; 2) it demonstrated performance superior 12 models including serial coupled model; 3) using LSTMs better than one principal component analysis. dataset includes district heater related variables, load-derived weather forecasts time factors affect loads. accuracy reflected lowest values mean absolute squared errors 0.571 0.662, respectively, highest R-squared value 0.942. therefore previously proposed demand expected be useful CHP plant management.

Language: Английский

Citations

130
CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany DOI Creative Commons
Fachrizal Aksan, Yang Li, Vishnu Suresh

et al.

Sensors, Journal Year: 2023, Volume and Issue: 23(2), P. 901 - 901

Published: Jan. 12, 2023

The massive installation of renewable energy sources together with storage in the power grid can lead to fluctuating consumption when there is a bi-directional flow due surplus electricity generation. To ensure security and reliability grid, high-quality prediction required. However, predicting remains challenge ever-changing characteristics influence weather on overcome these challenges, we present two most popular hybrid deep learning (HDL) models based combination convolutional neural network (CNN) long-term memory (LSTM) predict investigated cluster. In our approach, CNN-LSTM LSTM-CNN were trained different datasets terms size included parameters. aim was see whether dataset additional data affect performance proposed model flow. result shows that both achieve small error under certain conditions. While parameters training time accuracy HDL model.

Language: Английский

Citations

47
Proposing hybrid prediction approaches with the integration of machine learning models and metaheuristic algorithms to forecast the cooling and heating load of buildings DOI
He Dasi, Ying Zhang,

MD Faisal Bin Ashab

et al.

Energy, Journal Year: 2024, Volume and Issue: 291, P. 130297 - 130297

Published: Jan. 15, 2024

Language: Английский

Citations

24
A novel heat load prediction model of district heating system based on hybrid whale optimization algorithm (WOA) and CNN-LSTM with attention mechanism DOI
Xuyang Cui,

Junda Zhu,

Lifu Jia

et al.

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 133536 - 133536

Published: Oct. 1, 2024

Language: Английский

Citations

18
BE-LSTM: An LSTM-Based Framework for Feature Selection and Building Electricity Consumption Prediction on Small Datasets DOI
Weihao Wang, Hajime Shimakawa, Bo Jie

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 111910 - 111910

Published: Jan. 1, 2025

Language: Английский

Citations

2
Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model DOI
Sujan Ghimire, Thong Nguyen‐Huy, Ravinesh C. Deo

et al.

Sustainable materials and technologies, Journal Year: 2022, Volume and Issue: 32, P. e00429 - e00429

Published: May 20, 2022

Language: Английский

Citations

67
Outlet water temperature prediction of energy pile based on spatial-temporal feature extraction through CNN–LSTM hybrid model DOI

Weiyi Zhang,

Haiyang Zhou, Xiaohua Bao

et al.

Energy, Journal Year: 2022, Volume and Issue: 264, P. 126190 - 126190

Published: Nov. 28, 2022

Language: Английский

Citations

59
Principles, research status, and prospects of feature engineering for data-driven building energy prediction: A comprehensive review DOI
Zeyu Wang,

Lisha Xia,

Hongping Yuan

et al.

Journal of Building Engineering, Journal Year: 2022, Volume and Issue: 58, P. 105028 - 105028

Published: Aug. 6, 2022

Language: Английский

Citations

52
Data augmentation for improving heating load prediction of heating substation based on TimeGAN DOI
Yunfei Zhang, Zhihua Zhou, Junwei Liu

et al.

Energy, Journal Year: 2022, Volume and Issue: 260, P. 124919 - 124919

Published: Aug. 12, 2022

Language: Английский

Citations

40
A theory-guided deep-learning method for predicting power generation of multi-region photovoltaic plants DOI

Jian Du,

Jianqin Zheng, Yongtu Liang

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 118, P. 105647 - 105647

Published: Nov. 28, 2022

Language: Английский

Citations

39