RNN-based Forecasting of Indoor Temperature in a Naturally Ventilated Residential Building

Weng, Kui; Mourshed, Monjur

doi:10.26868/25222708.2019.211294

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…This modeling technique has become increasingly popular in recent years. There are also researchers in related fields using recurrent neural networks such as long short-term memory (LSTM) to predict temporal changes in temperature and achieving good results [11,[28][29][30], and LSTM is a special type of recurrent neural network (RNN) that is capable of learning long-term dependencies, which is more evident in the prediction of temporal sequences.…”

Section: Ref Time Granularitymentioning

confidence: 99%

Research on Online Temperature Prediction Method for Office Building Interiors Based on Data Mining

et al. 2023

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Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building.

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Section: Ref Time Granularitymentioning

confidence: 99%

Research on Online Temperature Prediction Method for Office Building Interiors Based on Data Mining

et al. 2023

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“…In recurrent neural networks (RNNs), the output from previous iteration is fed back as input to the current iteration for future forecasts. Weng and Mourshed (2019) developed a RNN model for forecasting indoor temperature F 41,1/2 using seasonal window opening schedules and ambient environmental conditions. Variables used: OAT, ORH, WS and direction, time, day, building thermal and window characteristics.…”

Section: Extreme Learning Machines Extreme Learning Machines (Elm) Al...mentioning

confidence: 99%

Indoor room temperature forecasting in buildings: a literature review (2001-21)

Pandey

Basu²

2022

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Purpose Building energy management systems use important information from indoor room temperature (IRT) forecasting to predict daily loads within smart buildings. IRT forecasting is a complex and challenging task, especially when energy demands are exponentially rising. The purpose of this paper is to review the relevant literature on indoor temperature forecasting in the past two decades and draw inferences on important methodologies with influencing variables and offer future directions. Design/methodology/approach The motivation for this work is based on the research work done in the field of intelligent buildings and energy related sector. The focus of this study is based on past literature on forecasting models and methodologies related to IRT forecasting for building energy management, with an emphasis on data-driven models (statistical and machine learning models). The methodology adopted here includes review of several journals, conference papers, reference books and PhD theses. Selected forecasting methodologies have been reviewed for indoor temperature forecasting contributing to building energy consumption. The models reviewed here have been earmarked for their benefits, limitations, location of study, accuracy along with the identification of influencing variables. Findings The findings are based on 62 studies where certain accuracy metrics and influencing explanatory variables have been reviewed. Linear models have been found to show explanatory relationships between the variables. Nonlinear models are found to have better accuracy than linear models. Moreover, IRT profiles can be modeled with enhanced accuracy and generalizability through hybrid models. Although deep learning models are found to have better performance for this study. Research limitations/implications This is accuracy-based study of data-driven models. Their run-time performance and cost implications review and review of physical, thermal and simulation models is future scope. Originality/value Despite the earlier work conducted in this field, there is a lack of organized and comprehensive evaluation of peer reviewed forecasting methodologies. Indoor temperature depends on various influencing explanatory variables which poses a research challenge for researchers to develop suitable predictive model. This paper presents a critical review of selected forecasting methodologies and provides a list of important methodologies along with influencing variables, which can help future researchers in the field of building energy management sector. The forecasting methods presented here can help to determine appropriate heating, ventilation and air-conditioning systems for buildings.

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Temp-AI-Estimator: Interior temperature prediction using domain-informed Deep Learning

Bischof,

Sprenger,

Riedel

et al. 2023

Energy and Buildings

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RNN-based Forecasting of Indoor Temperature in a Naturally Ventilated Residential Building

Cited by 3 publications

References 16 publications

Research on Online Temperature Prediction Method for Office Building Interiors Based on Data Mining

Research on Online Temperature Prediction Method for Office Building Interiors Based on Data Mining

Indoor room temperature forecasting in buildings: a literature review (2001-21)

Temp-AI-Estimator: Interior temperature prediction using domain-informed Deep Learning

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