Short-term energy use prediction of solar-assisted water heating system: Application case of combined attention-based LSTM and time-series decomposition

Heidari, Amirreza; Khovalyg, Dolaana

doi:10.1016/j.solener.2020.07.008

Cited by 70 publications

(16 citation statements)

References 32 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: 95%

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: 95%

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 most accurate models achieve a Pearson correlation coefficient (R) of about 0.8 which corresponds to an R² of 0.64. Heidari and Khovalyg [18] use a feed forward ANN as a baseline model and compare it to a Long Short Term Memory (LSTM) model, an attention-based LSTM model, and an attentionbased LSTM model using decomposed data for domestic hot water demand prediction. Compared to the feed forward ANN, the three LSTM-based models yield a 25 %, 28 %, or 41 % reduced Mean Absolute Error (MAE).…”

Section: Load Profile Prediction For Individual Consumers In Recent L...mentioning

confidence: 99%

Heat Load Profiles in Industry and the Tertiary Sector: Correlation with Electricity Consumption and Ex Post Modelling

Jesper¹,

Pag²,

Vajen³

et al. 2022

Preprint

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The accurate prediction of heat load profiles with a daily resolution is required for a broad range of applications such as potential studies, design, or predictive operation of heating systems. If the heat demand of a consumer is mainly caused by (production) processes independent of the ambient temperature, existing load profile prediction methods fail. To close this gap, this study develops two ex post machine learning models for the prediction of the heat demand with a daily resolution. The selected input features are commonly available to each consumer connected to public natural gas and electricity grids or operating an energy monitoring system: Ambient temperature, weekday, electricity consumption, and heat consumption of the last seven days directly before the predicted day. The study’s database covers electricity and natural gas consumption load profiles from 82 German consumers over a period of two years. Electricity and heat consumption correlate strongly with individual patterns for many consumers. Both, shallow and deep learning algorithms are evaluated. The highest accuracy is achieved by a Long Short Term Memory (LSTM) model with a median of R² of 0.94. The ex post model architecture makes the model suitable for automated anomaly detection in energy monitoring systems.

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“…Time series analysis and forecasting are currently at the beginning of their potential. It should be noted that, in the case of the construction of global models for the time series, the LSTM models allow the creation of high-performance models, both for point forecasts and long-term forecasts [16]. The fundamental question is then how powerful and accurate these newly introduced techniques are when compared with traditional approaches.…”

Section: Introductionmentioning

confidence: 99%

Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks

Mahjoub

Chrifi‐Alaoui

Marhic

et al. 2022

Sensors

111

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With the steep rise in the development of smart grids and the current advancement in developing measuring infrastructure, short term power consumption forecasting has recently gained increasing attention. In fact, the prediction of future power loads turns out to be a key issue to avoid energy wastage and to build effective power management strategies. Furthermore, energy consumption information can be considered historical time series data that are required to extract all meaningful knowledge and then forecast the future consumption. In this work, we aim to model and to compare three different machine learning algorithms in making a time series power forecast. The proposed models are the Long Short-Term Memory (LSTM), the Gated Recurrent Unit (GRU) and the Drop-GRU. We are going to use the power consumption data as our time series dataset and make predictions accordingly. The LSTM neural network has been favored in this work to predict the future load consumption and prevent consumption peaks. To provide a comprehensive evaluation of this method, we have performed several experiments using real data power consumption in some French cities. Experimental results on various time horizons show that the LSTM model produces a better result than the GRU and the Drop-GRU forecasting methods. There are fewer prediction errors and its precision is finer. Therefore, these predictions based on the LSTM method will allow us to make decisions in advance and trigger load shedding in cases where consumption exceeds the authorized threshold. This will have a significant impact on planning the power quality and the maintenance of power equipment.

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Short-term energy use prediction of solar-assisted water heating system: Application case of combined attention-based LSTM and time-series decomposition

Cited by 70 publications

References 32 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

Heat Load Profiles in Industry and the Tertiary Sector: Correlation with Electricity Consumption and Ex Post Modelling

Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks

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