District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

Xue, Guixiang; Pan, Yu; Lin, Tao; Song, Jiancai; Qi, Chengying; Wang, Z.

doi:10.3390/en12112122

Cited by 32 publications

(20 citation statements)

References 29 publications

(32 reference statements)

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“…City level models have received a lot of attention from the research community. The majority of the applications have been to heating-based target variables [49][50][51][52][53][54] and natural gas-based ( [55,56]). Common DL-based models applied to date include the DFFNN and the LSTM.…”

Section: City Levelmentioning

confidence: 99%

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A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

Runge

Zmeureanu

2021

Energies

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Buildings account for a significant portion of our overall energy usage and associated greenhouse gas emissions. With the increasing concerns regarding climate change, there are growing needs for energy reduction and increasing our energy efficiency. Forecasting energy use plays a fundamental role in building energy planning, management and optimization. The most common approaches for building energy forecasting include physics and data-driven models. Among the data-driven models, deep learning techniques have begun to emerge in recent years due to their: improved abilities in handling large amounts of data, feature extraction characteristics, and improved abilities in modelling nonlinear phenomena. This paper provides an extensive review of deep learning-based techniques applied to forecasting the energy use in buildings to explore its effectiveness and application potential. First, we present a summary of published literature reviews followed by an overview of deep learning-based definitions and techniques. Next, we present a breakdown of current trends identified in published research along with a discussion of how deep learning-based models have been applied for feature extraction and forecasting. Finally, the review concludes with current challenges faced and some potential future research directions.

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Section: City Levelmentioning

confidence: 99%

“…LSTM models have begun to be applied in order to forecast heating demands for district systems and shown promising results in references [52][53][54]. In 2020, Xue et al compared a variety of different algorithms in order to forecast the heating demand of a district system [54].…”

Section: City Levelmentioning

confidence: 99%

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

Runge

Zmeureanu

2021

Energies

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“…In the exploitation of deeper information, recurrent neural network(RNN) and long short-term memory(LSTM) have shown a higher capacity for analysis and processing. .RNN and LSTM can automatically extract and memorize the deep features of data, which are suitable for time series analysis and prediction and can achieve high prediction accuracy [11]. In Ref [12], RNN is used for heat load prediction of district heating and cooling systems, which can obtain a lower mean square error under non-stationary conditions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Spatial-Temporal Attention-LSTM Model for Heat Load Prediction

et al. 2020

Self Cite

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Accurate heat load prediction algorithm provides important support for the stable and efficient operation of smart district heating system(SDHS) and helps to realize energy saving and consumption reduction. However, previous researches on heat load prediction are mostly carried out on various regression analyses and modeling prediction, without considering the inherent time delay and spatial dependence between heat exchange stations during regulation. Therefore, a novel heat load prediction model based on the hybrid spatial-temporal attention long short-term memory (STALSTM) is proposed. The STALSTM model introduces the spatial dependence characteristics of heating pipe network into the heat load prediction model, and the influencing factors of heat consumption are considered comprehensively from the time and space dimensions. Then, the LSTM algorithm is used to memory the information of historical data sequence, and the attention mechanism is used to realize the adaptive estimation of the characteristic weight of each influencing factor, which improves the prediction accuracy. And in order to verify the effectiveness of the proposed model, a detailed experimental comparison is made between the STALSTM model and the state-of-the-art algorithms. The results show that the STALSTM model has the best prediction accuracy, and the correctness of introducing the spatial-temporal characteristics and the attention mechanism is also proved.

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“…This prediction structure exhibits a high compatibility with the existing prediction methods. In addition, short-term airconditioning load prediction algorithm based on the feature fusion long-short-term memory model was mooted in [20].…”

Section: Introductionmentioning

confidence: 99%

Air-Conditioning Load Forecasting for Prosumer Based on Meta Ensemble Learning

Chen¹,

2020

IEEE Access

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Accurate and reliable prediction of airconditioning load plays a significant role in prosumer energy management system (EMS), because airconditioning load accounts for a large proportion of the building's total energy consumption. This paper proposes a new meta ensemble learning method to realize short-term prediction of airconditioning load for prosumers. This method is a hybrid of meta ensemble learning and stacked auto-encoder (SAE). First, we design multiple different forecasting structures based on SAE to achieve point prediction of airconditioning loads. SAE is used to learn the deep features in airconditioning load data. Second, a new meta ensemble learning prediction model is proposed. Meta ensemble learning is used to learn the nonlinear features and invariant structures in data, and determine the coefficients of each SAE-based point forecaster. Finally, the prediction results of each point forecaster are aggregated and integrated to estimate the final airconditioning load prediction result. Airconditioning load data from a commercial building in Singapore are used to validate the feasibility and effectiveness of the proposed method, demonstrating that the proposed meta ensemble learning method is attractive in prosumer energy management. INDEX TERMS Airconditioning load, load prediction, meta learning, ensemble learning, prosumer energy management system.

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District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

Cited by 32 publications

References 29 publications

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

A Novel Hybrid Spatial-Temporal Attention-LSTM Model for Heat Load Prediction

Air-Conditioning Load Forecasting for Prosumer Based on Meta Ensemble Learning

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