Prediction of heating energy consumption with operation pattern variables for non-residential buildings using LSTM networks

Jang, Jinyoung; Han, Jinmog; Leigh, Seung-Bok

doi:10.1016/j.enbuild.2021.111647

Cited by 72 publications

(10 citation statements)

References 33 publications

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“…He and Tsang [26] achieved short-term load prediction of colleges and universities through integrating LSTM with the periodic pattern decomposition of time series. Similar methods based on LSTM and pattern decomposition can be seen in the energy prediction of solar-assisted water heating systems [27], in regional natural gas consumption prediction [28], in the power prediction of universities [26], in the heating energy prediction of non-residential buildings [29], etc. Wang, Yan, Li, Gao and Zhao [24] integrated local feature knowledge into a deep heterogeneous GRU model and implemented tool wear prediction in manufacturing.…”

Section: Introductionmentioning

confidence: 94%

A Spatio-Temporal Deep Learning Network for the Short-Term Energy Consumption Prediction of Multiple Nodes in Manufacturing Systems

et al. 2022

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Short-term energy prediction plays an important role in green manufacturing in the industrial internet environment and has become the basis of energy wastage identification, energy-saving plans and energy-saving control. However, the short-term energy prediction of multiple nodes in manufacturing systems is still a challenging issue owing to the fuzzy material flow (spatial relationship) and the dynamic production rhythm (temporal relationship). To obtain the complex spatial and temporal relationships, a spatio-temporal deep learning network (STDLN) method is presented for the short-term energy consumption prediction of multiple nodes in manufacturing systems. The method combines a graph convolutional network (GCN) and a gated recurrent unit (GRU) and predicts the future energy consumption of multiple nodes based on prior knowledge of material flow and the historical energy consumption time series. The GCN is aimed at capturing spatial relationships, with the material flow represented by a topology model, and the GRU is aimed at capturing dynamic rhythm from the energy consumption time series. To evaluate the method presented, several experiments were performed on the power consumption dataset of an aluminum profile plant. The results show that the method presented can predict the energy consumption of multiple nodes simultaneously and achieve a higher performance than methods based on the GRU, GCN, support vector regression (SVR), etc.

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Section: Introductionmentioning

confidence: 94%

A Spatio-Temporal Deep Learning Network for the Short-Term Energy Consumption Prediction of Multiple Nodes in Manufacturing Systems

et al. 2022

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“…The current research specifically focused on the shortterm forecasting of energy consumption, hence, the literature review primarily concentrated on short-term forecasting within commercial building applications [20,21]. After reviewing the state-of-the-art models, it was observed that common methods for such tasks included variations or hybrid models of autoregression integrated moving average [22,23], support vector machine [24][25][26], and LSTM [27,28].…”

Section: Related Work 21 Energy Consumption Forecasting In Commercial...mentioning

confidence: 99%

“…The LSTM network is then introduced to address this problem by incorporating nonlinear controls into the RNN cells [48]. Due to the ability to capture long-term dependencies without suffering from optimization hurdles, LSTM networks have been widely used in language modeling [49], text sentiment analysis [50], and time series forecasting [27,51,52].…”

Section: Long Short-term Memory (Lstm) Networkmentioning

confidence: 99%

Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning

Zhao,

Gomez-Rosero,

Nouraei

et al. 2024

Energies

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Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption.

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“…DNN was found to be the most efficient predictive model, motivating building designers to make informed choices and optimize structures. Jang et al [25] created three LSTM models to compare the effects of incorporating operation pattern data on prediction performance. The model using operation pattern data performed the best, with a CVRMSE of 17.6% and an MBE of 0.6%.…”

Section: Deepmentioning

confidence: 99%

Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings

Elhabyb,

Baina,

Bellafkih

et al. 2024

International Journal of Energy Research

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In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building’s energy consumption.

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Prediction of heating energy consumption with operation pattern variables for non-residential buildings using LSTM networks

Cited by 72 publications

References 33 publications

A Spatio-Temporal Deep Learning Network for the Short-Term Energy Consumption Prediction of Multiple Nodes in Manufacturing Systems

A Spatio-Temporal Deep Learning Network for the Short-Term Energy Consumption Prediction of Multiple Nodes in Manufacturing Systems

Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning

Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings

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