Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model

Fang, Zhen; Crimier, Nicolas; Scanu, Lisa; Midelet, Alphanie; Alyafi, Amr Alzouhri; Delinchant, Benoît

doi:10.1016/j.enbuild.2021.111053

Cited by 75 publications

(26 citation statements)

References 40 publications

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“…However, they reported that among cited external features, the outdoor temperature had the highest importance (i.e., importance factor of 42%). A similar finding was confirmed by Fang et al [24] whose correlation analysis outlined the future outdoor temperature as the most important exogenous variable, so we decided to keep it as an input. The main criterion adopted was selecting among available features only the ones that were related to the heating system which involved the target offices.…”

Section: The Datasetsupporting

confidence: 75%

“…As future work, this study can be extended by adapting current models to predict the building's internal temperature over longer time horizons to manage energy demand. Similar to other studies, such as that of Fang et al [24] and Attoue et al [21] this work could be extended by exploring the role of other characteristics, for example, occupancy, planned indoor activities or user behaviour, which could be measured, for example, through the presence of carbon dioxide in the air or the energy use of appliances. Furthermore, further studies could consist of producing models with controlled variables, such as radiant panel flow temperatures, based on predictions of observed variables (e.g., indoor temperatures) and driven by setpoint temperatures.…”

Section: Discussionmentioning

confidence: 91%

“…This is confirmed also by Attoue et al [21] that reported the same pattern, obtaining for the prediction horizon of 4 h an R = 0.8370 and MSE = 1.23783 versus an R = 0.9109 and MSE = 0.89078 for the 2 h forecasting. Fang et al [24], chose the Adam optimiser for their model because of its excellent performance in handling LSTM training dynamics and its rapid convergence speed, and tried to predict internal temperatures up to 7 days in advance, they observed a similar deterioration in prediction quality as time passed, especially after the first 48 h. Figure 11 shows that the Adam average RMSE continues to grow while enlarging the prediction time step so that a sort of turning point is reached and a change of optimiser is required to keep the average error around the reference value of 0.1 Celsius degrees. As mentioned before, in fact, Adagrad and especially SGD prove to better handle wider prediction time ranges.…”

Section: Model Tuningmentioning

confidence: 99%

“…The training was led by Bayesian regularisation exactly to improve generalisation. Recently, Fang et al [24] proposed also a multi-zone model that adopts a cross-series learning strategy, providing predictions up to 2-3 days ahead. Among the tested seq2seq models, the LSTM-dense model turned out to be the most useful for shortterm forecasting, while LSTM-dense-LSTM or LSTM-LSTM performed better for longer prediction horizons.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Models for Indoor Temperature Prediction Using Long Short Term Memory Networks—Case Study Energy Center

Già

Papurello

2022

Buildings

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In the European Union States, household energy usage accounts on average for 40% of overall energy consumption and is responsible for a considerable amount of carbon dioxide emissions. The urgent need to take concrete action to identify solutions that can ensure more effective usage of energy in households, both because of environmental and political reasons, has been repeatedly stated by the European Parliament. White box, grey box and black box predictive models were demonstrated to be a feasible approach to predict the indoor temperature to implement an effective energy management strategy. This study has the purpose of illustrating the potentiality of an LSTM Artificial Neural Network in a short and long-term prediction of the indoor temperature in 15 offices distributed on three storeys of an existing building (Energy Center of Turin (Italy)). The indoor temperature was predicted two hours, five hours and one entire day ahead. The performance of these algorithms has been evaluated not only based on two main criteria (i.e., Root Mean Squared Error and Mean Absolute error) but also by considering the adaptability of the model between the three floors and in terms of different years. Moreover, the proposed work explains how parameters affect performances, aiming to properly identify the optimal model structure. Current results indicate that these models can provide accurate predictions for all the proposed time scales and could all potentially be used for predictive control purposes to optimise the energy demand. The novelty of this study is to show that these models can only be trained on data for a limited period and a specific plane, and then be reliable in predicting indoor temperature, both for different planes and for random periods, taking into account temperature and relative humidity. Furthermore, input parameters are limited to indoor HVAC variables, to ensure acceptable predictions regardless of outdoor parameters availability. The only exception is the outdoor temperature, because of its undeniable and proven importance, it was retained as the only exogenous input variable. Based on current literature and temperature perception capabilities, the results were considered acceptable if the RMSE was less than 0.15 or better yet 0.10, which is equivalent to an inaccuracy between the predicted and actual indoor temperature of 0.15 °C/0.10 °C. On average, the models trained on the Energy Center database achieved an error of 0.1 °C in terms of RMSE.

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Section: The Datasetsupporting

confidence: 75%

Section: Discussionmentioning

confidence: 91%

Section: Model Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Models for Indoor Temperature Prediction Using Long Short Term Memory Networks—Case Study Energy Center

Già

Papurello

2022

Buildings

View full text Add to dashboard Cite

show abstract

“…The authors reported that r = 0.931 and MSE = 1.033 for training and r = 0.929 and MSE = 1.321 for the testing subset. Fang et al [49] predicted indoor temperature with the long short-term memory (LSTM) model in a low-energyconsumption building located in Grenoble, France. They obtained a mean MSE of around 0.25 • C.…”

Section: Validation Modellingmentioning

confidence: 99%

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

et al. 2021

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An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling.

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Study on the Prediction of Indoor Temperature and Thermal Load on Different Floors in a Community

Zhang

et al. 2021

Communications in Computer and Information Science

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Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model

Cited by 75 publications

References 40 publications

Hybrid Models for Indoor Temperature Prediction Using Long Short Term Memory Networks—Case Study Energy Center

Hybrid Models for Indoor Temperature Prediction Using Long Short Term Memory Networks—Case Study Energy Center

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

Study on the Prediction of Indoor Temperature and Thermal Load on Different Floors in a Community

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