Evaluation of ground source heat pump systems for residential buildings in warm Mediterranean regions: the example of Cyprus

Michopoulos, Apostolos; Voulgari, Vasiliki; Tsikaloudaki, Katerina; Zachariadis, Theodoros

doi:10.1007/s12053-016-9431-1

Cited by 31 publications

(19 citation statements)

References 45 publications

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“…Using EnergyPlus software, authors in [11] presented a feasibility analysis of the installation of ground source heat pump systems. The results of this analysis indicate that for Cyprus, the installation of ground source heat pump system is useful for a multi-family building and for a single building.…”

Section: State Of the Artmentioning

confidence: 99%

“…Various measures can be used for reduction in the energy consumption in buildings [6]. Some of them are: installation of ground source heat pump systems [11], combination of systems for heating, cooling, and power [12], using renewable energy sources such as solar, wind, hydro and biomass energy. Also, the construction of buildings which are self-sustainable is a solution for meeting the growing energy demand [13].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Energy Consumption in Buildings Using Support Vector Machine

et al. 2021

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The energy consumption of buildings can directly affect the buildings users' budget and their satisfaction with the investment in the property. Vice versa, buildings energy consumption has a social implication on the buildings' users. Additionally, building energy consumption is connected with the buildings influence on the environment due to the CO2 emission. Thus, having a model for energy usage prediction is of crucial importance. Data for sixty real-built buildings were collected. Using support vector machine, a model was developed for prediction of energy consumption. The mean absolute percentage error of the model is 2,44% and the coefficient of determination of the model R 2 is 94,72%, which expresses the global fit of the model. The model is useful for all participants in the designs of buildings, particularly in the early phases. It can serve as a decision support model during the process of selection of optimal building design.

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Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Energy Consumption in Buildings Using Support Vector Machine

et al. 2021

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“…The hot climate region represents the southern coastal areas of the island, the moderate climate is characteristic for the mainland areas, while the cold region represents the mountainous areas of the island. With the aid of these data, dynamic simulations of the characteristic building typologies were performed using the EnergyPlus software, an acclaimed dynamic energy simulation software primarily used for modelling building energy efficiency [14], taking into consideration typical indoor conditions and usage patterns, retrieved by previous analysis [15]. Other input data, as illustrated in Figure 1, are:…”

Section: Exploring the Energy Efficiency Potential Through Engineerinmentioning

confidence: 99%

“…It is worth noting that overall the 2EMRS model uses statistical data provided or extracted from the surveys or statistics conducted by CyStat, which are the only available survey data in Cyprus. Moreover, the simulations of the characteristic buildings, which were performed with the aid of EnergyPlus, are based on typical patterns that have been validated in a previous analysis [15].…”

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confidence: 99%

Determination of Cost-Effective Energy Efficiency Measures in Buildings with the Aid of Multiple Indices

et al. 2018

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Energy refurbishments of buildings can substantially contribute to economy-wide energy efficiency improvements, leading to decarbonisation and additional sustainability benefits. Prioritising the most economically promising investments is not straightforward because apart from cost-effectiveness calculations, several real-world constraints have to be taken into account. This paper describes an approach to assess the economically viable energy efficiency potential in the building sector of the Mediterranean island of Cyprus, with a combination of detailed engineering modelling, cost-effectiveness calculations and real-world considerations of budgetary, technical, behavioural and market constraints. We examine diverse cost-effectiveness indices and come up with a proposal for prioritising specific energy investments such as the installation of heat pumps, insulation of roofs, and replacement of lighting and electronic equipment-without however ignoring other measures that may be economically less favourable but can realistically be implemented in a limited number of buildings. Finally we address the governance of energy efficiency policies, focusing on weaknesses of the current regulatory environment in Cyprus, which can be generalised for many other countries facing similar dilemmas.

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“…During the stage of selecting the optimal design of the new building or old building reconstruction/renovation, numerous measures/solutions can be used for reducing the building's energy consumption. Some of them are: system with heat pump from ground source (Michopoulos et al, 2016); renewable energy systems usage and their combination or combination of systems for heating, power and cooling; system installation for energy management, etc. (Farhat et al, 2014;Rasool et al, 2015;Kialashaki, 2018).…”

Section: Introductionmentioning

confidence: 99%

Application of Soft Computing Methods to Increase Sustainability in Construction

Petruseva¹,

Pancovska²

2020

XV International Conference on Durability of Building Materials and Components. eBook of Proceedings

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In the last three decades the soft computing methods were used by the research community in almost every branch of construction, providing successful and convenient solutions for different problems in civil engineering. This paper presents some of the applications of these methodsespecially neural networks (NN) and support vector machine (SVM)-in sustainable construction, i.e. its economic, social and environmental aspects. Soft computing applications were made in the last several years by our research team at the Faculty of Civil Engineering in Skopje, N. Macedonia, in collaboration with other authors from our and other countries. Several predictive models were developed using: general regression neural network (GRNN), support vector machine (SVM) and radial basis function neural network (RBF NN), using predictive modelling software DTREG. Applications of these models cover most of the aspects of sustainability in construction. Models were focused on predicting: road structure construction costs, bidding price in construction, sustainability assessment at early facilities design phase, predicting construction cost and construction time and predicting consumption of energy in buildings. Some of the mentioned developed predictive models are hybrid, composed of process-based and data driven models which contributed very much to the improvement of the accuracy of the predicting. The general conclusion is that the soft computing methods are a useful tool for developing models in the area of all aspects of sustainability and their application can lead to increasing sustainability in construction.

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Evaluation of ground source heat pump systems for residential buildings in warm Mediterranean regions: the example of Cyprus

Cited by 31 publications

References 45 publications

Prediction of Energy Consumption in Buildings Using Support Vector Machine

Prediction of Energy Consumption in Buildings Using Support Vector Machine

Determination of Cost-Effective Energy Efficiency Measures in Buildings with the Aid of Multiple Indices

Application of Soft Computing Methods to Increase Sustainability in Construction

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