Regression models for predicting UK office building energy consumption from heating and cooling demands

Korolija, Ivan; Yi, Zhang; Marjanovic-Halburd, Ljiljana; Hanby, V. I.

doi:10.1016/j.enbuild.2012.12.005

Cited by 105 publications

(52 citation statements)

References 11 publications

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“…Korolija et al [13] uses building type, orientation, building fabrics, glazing ratio, glazing coating, overhang, day lighting internal source, and HVAC system for generating cooling and heating demand. And they apply regression analysis to the generated data for modeling energy consumption.…”

Section: Difficulties Of User's Access In Using Existing Energy Consumentioning

confidence: 99%

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Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR

2018

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Abstract:In many countries, DR (Demand Response) has been developed for which customers are motivated to save electricity by themselves during peak time to prevent grand-scale blackouts. One of the common methods in DR, is CPP (Critical Peak Pricing). Predicting energy consumption is recognized as one of the tool for dealing with CPP. There are a variety of studies in developing the model of energy consumption, which is based on energy simulation, data-driven model or metamodelling. However, it is difficult for general users to use these models due to requirement of various sensing data and expertise. And it also takes long time to simulate the models. These limitations can be an obstacle for achieving CPP's purpose that encourages general users to manage their energy usage by themselves. As an alternative, this research suggests to use open data and GA (Genetic Algorithm)-SVR (Support Vector Regression). The model is applied to a hospital in Korea and 34,636 data sets (1 year) are collected while 31,756 (11 months) sets are used for training and 2880 sets (1 month) are used for validation. As a result, the performance of proposed model is 14.17% in CV (RMSE), which satisfies the Korea Energy Agency's and ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) error allowance range of ±30%, and ±20% respectively.

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Section: Difficulties Of User's Access In Using Existing Energy Consumentioning

confidence: 99%

“…There are a variety of studies in developing the model of energy consumption [6][7][8][9][10][11][12][13][14], which is based on energy simulation or data-driven model, and metamodeling. However, it is difficult for general user to use these models due to requirement of various sensing data and expertise.…”

Section: Introductionmentioning

confidence: 99%

Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR

2018

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“…According to Yu et al [27], some innovative techniques including machine learning, data mining, and discovery in database have been successfully applied to building energy consumption. In this regard, , Korolija et al [15] developed regression models to predict the annual heating, cooling, and electrical auxiliary energy consumption of five different types of HVAC systems and two chilled ceiling systems for office buildings in the UK. Regarding EIA [8], the majority of energy consumption in commercial buildings is related to space heating, cooling, and lighting.…”

Section: Design Process For Smart Grid and Smart Energymentioning

confidence: 99%

Investigating HCI Challenges for Designing Smart Environments

Pourzolfaghar

Helfert

2016

HCI in Business, Government, and Organizations: Information Systems

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Abstract. With the advancement of technologies related to 'Internet of Things', we are moving towards environments characterised by full integration and semantics. Various environments are often summarized with terms such as 'Smart City', 'Smart Home', 'Smart Buildings' or 'Smart Commerce'. In the meantime, technologies and standards for interoperability have been developed. However, to realise the full potential one remaining challenge is the design, integration and interoperability of many elements into a smart environment. In order to address this challenge, researchers have proposed concepts for Information Systems Design and Enterprise Architectures. By inspecting interaction challenges -in particular activities in which Humans are involved-during the design process, we endeavour in this paper to identify key challenges for designing smart environments. In order to address the challenges we propose a conversational approach that supports the main design phases and allows professionals to interact during the design phases for smart environments.

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“…In this regard, [6] emphasised on the importance of the capability to reliably estimate the buildings' energy consumption. For this purpose, many researchers utilised machine learning and data mining [27], or regression models [11] to predict and estimate building energy consumption. Some other researches e.g.…”

Section: Building Information and Smart Energymentioning

confidence: 99%

Utilising Latent Data in Smart Buildings: A Process Model to Collect, Analyse and Make Building Data Accessible for Smart Industries

Pourzolfaghar

Helfert

2017

Annals of Computer Science and Information Systems

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Abstract-Smart buildings are embedded with large amounts of latent data from different sources, e.g. IoT devices, sensors, and the like. Integration of this latent data with the buildings information can highly impact efficiency of the services provided by various industries, e.g. facility management companies, utility companies, smart commerce, and so forth. To manage buildings information, diverse technologies such as Building Information Modelling (BIM) technology have been developed and changed the traditional approaches. Notwithstanding a plethora of research in this area, potential users of this information, such as facility management companies, are still unable to fully benefit from the building information. This is due to the fact that various information and data have been heterogeneously scattered across various sources. To overcome this challenge, this research follows the design science approach to propose a process model to enable facility management industry to access the integration of buildings information with live data. The presented process model is introduced thoroughly by explaining the required steps to collect and integrate and preserve the integrated information and data. The evaluation of the process model was undertaken via the employment of a focus group session with the construction professionals, the IoT experts, and the data analysts. Also, this paper elaborates on two industrial use-cases to demonstrate how having access to the building information effectively affects the other industries. The outcome of this research provides an open access to the integration of building information and live data for diverse range of users.

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Regression models for predicting UK office building energy consumption from heating and cooling demands

Cited by 105 publications

References 11 publications

Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR

Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR

Investigating HCI Challenges for Designing Smart Environments

Utilising Latent Data in Smart Buildings: A Process Model to Collect, Analyse and Make Building Data Accessible for Smart Industries

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