Forecasting indoor temperatures during heatwaves using time series models

Gustin, Matej; McLeod, Robert S.; Lomas, Kevin J.

doi:10.1016/j.buildenv.2018.07.045

Cited by 48 publications

(28 citation statements)

References 31 publications

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“…The initial criteria to select the input parameters was to include variables that can be obtained from weather services online and from relatively simple user-provided feedback. The literature review presented in the introduction showed that building-related parameters, outdoor climate and occupant behaviour had an effect on indoor temperature values in several studies [13,14,15,16,17,18,19,20,21,22,23,24,25,26]. A comprehensive database extracted from field studies [27,28] was used to train and test the method.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies developed more advanced techniques for indoor temperature prediction than merely linear regression models. Some of them used Time-Series analysis [20] and others used a combination between Time-Series and Artificial Neural Networks (ANN) [21,22,23,24]. Time-Series is an approach used for data forecasting based on statistical analysis of measured values over a defined period.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Indoor Air Temperature Using Weather Data and Simple Building Descriptors

Aguilera

Andersen

Toftum

2019

IJERPH

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Non-optimal air temperatures can have serious consequences for human health and productivity. As the climate changes, heatwaves and cold streaks have become more frequent and intense. The ClimApp project aims to develop a smartphone App that provides individualised advice to cope with thermal stress outdoors and indoors. This paper presents a method to predict indoor air temperature to evaluate thermal indoor environments. Two types of input data were used to set up a predictive model: weather data obtained from online weather services and general building attributes to be provided by App users. The method provides discrete predictions of temperature through a decision tree classification algorithm. The data used to train and test the algorithm was obtained from field measurements in seven Danish households and from building simulations considering three different climate regions, ranging from temperate to hot and humid. The results show that the method had an accuracy of 92% (F1-score) when predicting temperatures under previously known conditions (e.g., same household, occupants and climate). However, the performance decreased to 30% under different climate conditions. The approach had the highest performance when predicting the most commonly observed indoor temperatures. The findings suggest that it is possible to develop a straightforward and fairly accurate method for indoor temperature estimation grounded on weather data and simple building attributes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Indoor Air Temperature Using Weather Data and Simple Building Descriptors

Aguilera

Andersen

Toftum

2019

IJERPH

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“…"Grey-box" models can be also converted to "black-box" (i.e., statistical and machine learning models) for specific applications, for example control [204] or monitoring of internal conditions [205,206]. "Black box" models are computationally efficient but they need to be trained on data before being deployed.…”

Section: Harmonizing Methodologies To Analyse Energy Performancementioning

confidence: 99%

Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector

2021

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Decarbonisation and efficiency goals set as a response to global warming issue require appropriate decision-making strategies to promote an effective and timely change in energy systems. Conceptualization of change is a relevant part of energy transitions research today, which aims at enabling radical shifts compatible with societal functions and market mechanisms. In this framework, construction sector can play a relevant role because of its energy and environmental impact. There is, however, the need to move from general instances to specific actions. Open data and open science, digitalization and building data interoperability, together with innovative business models could represent enabling factors to accelerate the process of change. For this reason, built environment research has to address the co-evolution of technologies and human behaviour and the analytical methods used for this purpose should be empirically grounded, transparent, scalable and consistent across different temporal/spatial scales of analysis. These features could potentially enable the emergence of “ecosystems” of applications that, in turn, could translate into projects, products and services for energy transitions in the built environment, proposing innovative business models that can stimulate market competitiveness. For these reasons, in this paper we organize our analysis according to three levels, from general concepts to specific issues. In the first level, we consider the role of building energy modelling at multiple scales. In the second level, we focus on harmonization of methods for energy performance analysis. Finally, in the third level, we consider emerging concepts such as energy flexibility and occupant-centric energy modelling, considering their relation to monitoring systems and automation. The goal of this research is to evaluate the current state of the art and identify key concepts that can encourage further research, addressing both human and technological factors that influence energy performance of buildings.

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“…In this section, two forecasting models are compared: static seasonal autoregressive moving average models (SARIMA), and dynamic linear models (DLM). SARIMA models are parsimonious and have been shown to be effective in similar settings, that is, for forecasting extreme temperatures (Gustin et al, 2018). In comparison, DLM utilizes far more parameters which adds flexibility in the modeling, but may result in less stable forecasts.…”

Section: Forecasting Methodologymentioning

confidence: 99%

Digital twin of an urban-integrated hydroponic farm

Jans-Singh

Leeming

Choudhary

et al. 2020

DCE

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This paper presents the development process of a digital twin of a unique hydroponic underground farm in London, Growing Underground (GU). Growing 12x more per unit area than traditional greenhouse farming in the UK, the farm also consumes 4x more energy per unit area. Key to the ongoing operational success of this farm and similar enterprises is finding ways to minimize the energy use while maximizing crop growth by maintaining optimal growing conditions. As such, it belongs to the class of Controlled Environment Agriculture, where indoor environments are carefully controlled to maximize crop growth by using artificial lighting and smart heating, ventilation, and air conditioning systems. We tracked changing environmental conditions and crop growth across 89 different variables, through a wireless sensor network and unstructured manual records, and combined all the data into a database. We show how the digital twin can provide enhanced outputs for a bespoke site like GU, by creating inferred data fields, and show the limitations of data collection in a commercial environment. For example, we find that lighting is the dominant environmental factor for temperature and thus crop growth in this farm, and that the effects of external temperature and ventilation are confounded. We combine information learned from historical data interpretation to create a bespoke temperature forecasting model (root mean squared error < 1.3°C), using a dynamic linear model with a data-centric lighting component. Finally, we present how the forecasting model can be integrated into the digital twin to provide feedback to the farmers for decision-making assistance.

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Forecasting indoor temperatures during heatwaves using time series models

Cited by 48 publications

References 31 publications

Prediction of Indoor Air Temperature Using Weather Data and Simple Building Descriptors

Prediction of Indoor Air Temperature Using Weather Data and Simple Building Descriptors

Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector

Digital twin of an urban-integrated hydroponic farm

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