Short-term energy consumption prediction method for educational buildings based on model integration

Cao, Wen‐Qiang; Yu, Junqi; Chao, M.; Wang, Jingqi; Yang, Siyuan; Zhou, Meng; Wang, Meng

doi:10.1016/j.energy.2023.128580

Cited by 25 publications

(7 citation statements)

References 36 publications

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“…With the continuous advancement of building information technology, a series of advanced data management systems, such as Building Information Modeling (BIM) and intelligent building management systems, are widely adopted. These systems systematically collect and store various data generated during the operation and maintenance of buildings [16,17]. Leveraging rich historical building energy data, data-driven methods based on machine learning algorithms or statistical analysis principles have achieved precise predictions of building energy consumption [18].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Method for Industrial Buildings Based on Signal Decomposition and Composite Prediction Model

Zhao,

Fan

2024

Sustainability

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Accurately predicting the cold load of industrial buildings is a crucial step in establishing an energy consumption management system for industrial constructions, which plays a significant role in advancing sustainable development. However, due to diverse influencing factors and the complex nonlinear patterns exhibited by cold load data in industrial buildings, predicting these loads poses significant challenges. This study proposes a hybrid prediction approach combining the Improved Snake Optimization Algorithm (ISOA), Variational Mode Decomposition (VMD), random forest (RF), and BiLSTM-attention. Initially, the ISOA optimizes the parameters of the VMD method, obtaining the best decomposition results for cold load data. Subsequently, RF is employed to predict components with higher frequencies, while BiLSTM-attention is utilized for components with lower frequencies. The final cold load prediction results are obtained by combining these predictions. The proposed method is validated using actual cold load data from an industrial building, and experimental results demonstrate its excellent predictive performance, making it more suitable for cold load prediction in industrial constructions compared to traditional methods. By enhancing the accuracy of cold load predictions. This approach not only improves the energy efficiency of industrial buildings but also promotes the reduction in energy consumption and carbon emissions, thus contributing to the sustainable development of the industrial sector.

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Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Method for Industrial Buildings Based on Signal Decomposition and Composite Prediction Model

Zhao,

Fan

2024

Sustainability

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“…As global climate change has been drawing increasing concern, building and construction industry are caching growing attentions, since they accounts for dominant energy usage (Cao et al, 2023) and greenhouse gas emissions (Christopher et al, 2023), leading to accelerating global warming (Tirelli and Besana, 2023). In China, buildings are responsible for over 30% primary energy consumptions.…”

Section: Introductionmentioning

confidence: 99%

Modelling and onsite testing on dynamic thermal responses of built environment in passive solar rooms on Tibetan plateau

Zhang,

Han,

2024

Front. Energy Res.

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It of great importance in assessing built thermal environment level and evaluating corresponding indoor air conditioning demand for energy conservation in construction sectors. Nevertheless, because of the unique meteorological features in plateau area, classical building performance simulation approach contributes to thermal performance evaluation errors since most design codes or standards relies on low attitude regions. In this paper, a modified and improved dynamic thermal design model is put forward for built environment and energy consumption estimation for passive buildings for plateau buildings. Moreover, the simplified experiment is set up to monitor dynamic thermal responses for modelling building. The testing validation illustrate that the onsite measurement accuracy level is quite acceptable for engineering applications with less than 30% relative change range coefficient. Besides, the experiment data demonstrates that window-to-wall ratios, architectural orientation, thermal insulation coefficients, have substantial impacts for solar heat gains in plateau buildings. The study renders building design guidance for energy conservation in high altitude plateau areas.

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“…Machine learning-based energy consumption prediction models encompass several types, such as Support Vector Machines (SVMs) [21,22], Artificial Neural Networks (ANNs) [23,24], and deep learning (DL) methods [25,26]. Among them, deep learning methods exhibit advantages in the field of energy consumption prediction compared to ordinary machine learning methods [27]. They can automatically learn complex nonlinear relationships, adapt to large-scale data, effectively capture spatiotemporal relationships, and enable end-to-end learning.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Energy Consumption Prediction of Large Public Buildings Combined with Data Feature Engineering and Bilstm-Attention

Tian,

Chen,

Zhao

2024

Applied Sciences

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Accurate building energy consumption prediction is a crucial condition for the sustainable development of building energy management systems. However, the highly nonlinear nature of data and complex influencing factors in the energy consumption of large public buildings often pose challenges in improving prediction accuracy. In this study, we propose a combined prediction model that combines signal decomposition, feature screening, and deep learning. First, we employ the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose energy consumption data. Next, we propose the Maximum Mutual Information Coefficient (MIC)-Fast Correlation Based Filter (FCBF) combined feature screening method for feature selection on the decomposed components. Finally, the selected input features and corresponding components are fed into the Bi-directional Long Short-Term Memory Attention Mechanism (BiLSTMAM) model for prediction, and the aggregated results yield the energy consumption forecast. The proposed approach is validated using energy consumption data from a large public building in Shaanxi Province, China. Compared with the other five comparison methods, the RMSE reduction of the CEEMDAN-MIC-FCBF-BiLSTMAM model proposed in this study ranged from 57.23% to 82.49%. Experimental results demonstrate that the combination of CEEMDAN, MIC-FCBF, and BiLSTMAM modeling markedly improves the accuracy of energy consumption predictions in buildings, offering a potent method for optimizing energy management and promoting sustainability in large-scale facilities.

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Short-term energy consumption prediction method for educational buildings based on model integration

Cited by 25 publications

References 36 publications

Short-Term Load Forecasting Method for Industrial Buildings Based on Signal Decomposition and Composite Prediction Model

Short-Term Load Forecasting Method for Industrial Buildings Based on Signal Decomposition and Composite Prediction Model

Modelling and onsite testing on dynamic thermal responses of built environment in passive solar rooms on Tibetan plateau

Short-Term Energy Consumption Prediction of Large Public Buildings Combined with Data Feature Engineering and Bilstm-Attention

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