Use of Machine Learning Methods for Indoor Temperature Forecasting

Ramadan, Lara; Shahrour, Isam; Mroueh, Hussein; Chehade, Fadi Hage

doi:10.3390/fi13100242

Cited by 12 publications

(5 citation statements)

References 77 publications

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“…Using cloud services for agriculture reduces the burden of numerous devices to be installed in environments with high temperatures and increased humidity. Thus, the usage of integrated predictive analytics as a cloud service is an alternative for reducing the number of sensors while also providing parameter analysis and forecasting services [12].…”

Section: Related Workmentioning

confidence: 99%

GreenLab, an IoT-Based Small-Scale Smart Greenhouse

Volosciuc,

Bogdan,

Blajovan

et al. 2024

Future Internet

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In an era of connectivity, the Internet of Things introduces smart solutions for smart and sustainable agriculture, bringing alternatives to overcome the food crisis. Among these solutions, smart greenhouses support crop and vegetable agriculture regardless of season and cultivated area by carefully controlling and managing parameters like temperature, air and soil humidity, and light. Smart technologies have proven to be successful tools for increasing agricultural production at both the macro and micro levels, which is an important step in streamlining small-scale agriculture. This paper presents an experimental Internet of Things-based small-scale greenhouse prototype as a proof of concept for the benefits of merging smart sensing, connectivity, IoT, and mobile-based applications, for growing cultures. Our proposed solution is cost-friendly and includes a photovoltaic panel and a buffer battery for reducing energy consumption costs, while also assuring functionality during night and cloudy weather and a mobile application for easy data visualization and monitoring of the greenhouse.

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Section: Related Workmentioning

confidence: 99%

GreenLab, an IoT-Based Small-Scale Smart Greenhouse

Volosciuc,

Bogdan,

Blajovan

et al. 2024

Future Internet

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“…Delcroix et al (2021) proposed an autoregressive neural network model to simulate indoor temperatures with a coefficient of determination R 2 of 0.824 and RMSE of 1.11°C. Ramadan et al (2021) compared the ability of seven machine learning algorithms including ANNs, MLPs and gray box model to predict the IRT of a closed room. The study was conducted in a commercial building in Montreal, Canada.…”

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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“…Ramadan et al [47] made use of seven machine-learning models in their indoor temperature forecasting for a room of the Laboratory of Civil Engineering and Geo-Environment at Lille University, France. They concluded that the best prediction was obtained by ANN and extra trees (ET) regressor methods.…”

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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Use of Machine Learning Methods for Indoor Temperature Forecasting

Cited by 12 publications

References 77 publications

GreenLab, an IoT-Based Small-Scale Smart Greenhouse

GreenLab, an IoT-Based Small-Scale Smart Greenhouse

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

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

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