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Energy consumption for general simulation versus actual energy bills and calibrated simulation versus actual energy with linear regressions.
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User behaviour influences the energy consumption of domestic properties with different range of variations and this has an effect on the results of building simulations based on default or general values, as opposed to implementing user behaviour. The aim of this paper is to evaluate and quantify the effect of implementing user behaviour in buildin...
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... temperature (ºC) The results of plotting the energy consumption from the general simulation (scenario 1) versus the energy consumption from energy bills are presented in Fig. 2. Furthermore, a linear regression is plotted. The linear regression formula and coefficient of determination for the regression general simulation versus actual energy bills are indicated in Eq. (1) ...
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... a similar way and for easiness of comparison, the results of energy consumption from the calibrated simulation (Scenario 2), considering the three parameters for user behaviour, versus the energy consumption from energy bills are as well shown in Fig. 2. The linear regression formula and coefficient of determination for the regression calibrated simulation versus actual energy bills are indicated in Eq. (2) ...
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... presented in Table 5 and Fig. 2, the results for energy consumption for space heating and DHW obtained by the general dynamic building simulation are very much far out form the actual energy consumption obtained by the energy bills. This is very much in accordance to the literature [26]- [29] and expected from treating the building simulations as general parameters ...
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... to the literature [26]- [29] and expected from treating the building simulations as general parameters taking from Table 3, without considering the user behaviour for the building simulation. More specifically, parameters in relation to the heating set points and use of space heating, as well as the estimation of DHW requirements. Eq. (1) and Fig. 2 provides an inside regarding how scatter the correlation between general simulation and actual bills is, with a coefficient of determination R 2 = 0.02342, meaning that less than 3 % of the actual energy consumption of the properties can be explained by the general building simulation. 316 Furthermore, analysing the percentage ...
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... shown in Table 5 and Fig. 2, the correlation between the energy consumption from the calibrated building simulation and the actual energy bills has improved massively, providing an argument that by considering only three parameters to adapt the building simulations, a much better estimation can be assure from the dynamic building simulation. The coefficient of ...
Citations
... Furthermore, there is no doubt that building energy performance is influenced by both building design and the overall effects of climate change [6]. Retrofitting of buildings was highlighted by Jimenez-Bescos and Oregi as an important aspect to consider reducing carbon emissions [7]. Nevertheless, by understanding the influence of climate change to building stock and estimating the heating and cooling demand, city planners and policymakers could get information, in order to create environmental goals for the reduction of the carbon emissions that actually help to tackle the complicated situation of climate change and global warming. ...
There is no doubt that during recent years, the developing countries are in urgent demand of energy, which means the energy generation and the carbon emissions increase accumulatively. The 40 % of the global energy consumption per year comes from the building stock. Considering the predictions regarding future climate due to climate change, a good understanding on the energy use due to future climate is required. The aim of this study was to evaluate the impact of future weather in the heating demand and carbon emissions for a group of buildings at district level, focusing on two areas of London in the United Kingdom. The methodological approach involved the use of geospatial data for the case study areas, processed with Python programming language through Anaconda and Jupyter notebook, generation of an archetype dataset with energy performance data from TABULA typology and the use of Python console in QGIS to calculate the heating demand in the reference weather data, 2050 and 2100 in accordance with RCP 4.5 and RCP 8.5 scenarios. A validated model was used for the district level heating demand calculation. On the one hand, the results suggest that a mitigation of carbon emissions under the RCP4.5 scenario will generate a small decrease on the heating demand at district level, so slightly similar levels of heating generation must continue to be provided using sustainable alternatives. On the other hand, following the RCP 8.5 scenario of carbon emission carrying on business as usual will create a significant reduction of heating demand due to the rise on temperature but with the consequent overheating in summer, which will shift the energy generation problem. The results suggest that adaptation of the energy generation must start shifting to cope with higher temperatures and a different requirement of delivered energy from heating to cooling due to the effect of climate change.
... However, simulation without calibration can lead to substantial error in the calculated energy consumption. If properly calibrated, the building energy simulations can estimate the real energy consumption 90 % of the time [12]. In modelling, various uncertainties (specification, numerical, scenario, heuristic), inter-model variability, or even post-design changes can be important, increasing the performance gap. ...
Occupant behaviour is a field, that has always been of great interest to researchers. It could significantly modify the operation of the building and the user’s energy needs, and it is also difficult to model it according to reality. Evaluation of measurements is a crucial step to calibrate dynamic simulations. Our goal was to analyse the indoor comfort conditions according to measurements, particularly in summertime, and find what solution closes the performance gap between the measured and simulated results. In this research, we investigated an apartment building that underwent an energy efficiency renovation. We have installed a weather station and monitoring sensors in selected apartments, with which we monitored the temperature, relative humidity, and CO2 values of certain rooms, the presence of the inhabitants and the window opening and the operation of shading. In this paper, we focus on the monitoring and simulation results of the topmost apartment. The results can help us better understand how buildings work and how to implement user behaviour in dynamic simulations, how to calibrate the model according to measurements and make suggestions to increase the comfort of the residents.
... The Ed H can differ due to many factors, being the user behaviour and the base temperature the most influential ones according to previous studies [40][41][42][43] and being necessary to consider that may be changes in the Ed H of the building during the time. Furthermore, the climate change can alter the Ed H of the residential buildings reducing it [44]. ...
The most recent regulations, as well as the scientific studies, remark the importance of the evaluation of the entire life cycle on building renovations, relative to the environmental impact and economic feasibility, making the Life Cycle Assessment (LCA) the prioritizing analysis. The objective of the study is to develop a simplified methodology for the environmental and economic assessment of residential building renovations with life cycle approach. For this, a script-based tool-kit is developed: the first tool optimizes the thickness of envelope insulation based strategies; the second tool is for the prioritization of strategies by assessing their environmental performance and economic feasibility. In order to follow the objective, the development of the two tools is presented: both tools follow a parallel scheme where the input parameters are required by an excel file and the calculation script provides the results automatically by exporting the results excel file. The evaluation provides the quantification of the relative environmental improvement with the net energy ratio (NER), and the economic feasibility by the financial indicator of internal rate of return (IRR). The tool-kit is applied in a case study of a multifamily residential building. The results show, on the one hand, that the usability of the tool-kit can be determinant in the decision-making of stakeholders; and in the other hand, the importance of carrying out a dynamic assessment, taking into account the variation of the results caused by the uncertain parameters that differ in time. Moreover, the tool-kit can assist the development of cost-effective decarbonisation strategies.
... In the Social and Wellness & Health aspects perceptive data is also used, by non-technical indicators obtained by surveys or interviews; it can be a determinant data source as in the study of Jimenez-Bescos and Oregi, were they used a questionnaire to reinforce the energy computation estimation [53]. In ENERPAT, participation of stakeholders and citizens was an important pillar against the energy poverty and following the citizen acceptance [9] working on the social field. ...
Energy retrofit of existing buildings is one of the main keys to achieve European Union’s decarbonising objectives defined in the European Green Deal. In order to proceed into them, European policy has been adapted and several research projects are developed. The aim of this paper is to analyse the assessment methodology of the research projects, setting up the overview of the assessed fields and the criteria followed to perform and evaluate each project. As working methodology, 18 projects have been studied, firstly characterising by the main parameters and afterwards analysing the assessment followed by each one. This analysis is decomposed into five parameters: the assessment scope, reflecting the fields covered by the project’s assessment; data source, the nature of the data; verification, use of data verification strategies; and implementation of life cycle thinking in the assessment methodology. The research shows that although the projects have their bases in the EU energetic targets they also cover a wider scope, assessing many fields and combining many sources of data. However, despite the large knowledge already defined by many projects, there is a lack of global and complete roadmap to be followed.
... In a study focusing on the impact of user behaviour and how to incorporate user behaviour on building simulation, Jimenez-Bescos et al. [22] have noticed a reduction in comfort due to future weather will generate a change in behaviour from users to adapt to higher temperatures. ...
With the higher pace of climate change, temperatures are rising each year, resulting in various effects on the thermal status of buildings. This paper takes the opportunity of analysing different scenarios of greenhouse gas (GHG) emissions using hourly weather data of future projections by implementing EPW weather files on EnergyPlus software dynamic simulations, coupled with architectural science methods of climate analysis, to test the effect of high and medium-high emission scenarios for the 2050s and 2080s future timelines on thermal comfort range, passive zones potential, and heating/cooling periods, as compared to the weather data from 2003-2017. Simulations results have shown a remarkable effect on the scale of daily cooling hours and monthly coverage under the high GHG emission scenario, expanding its range by 60 %, with 6 hours on summer peak days and 3 months/year, as well as an annual decrease in heating period by 33.3 %. Thermal comfort zones of tested periods have also witnessed an alternation, translating the effect on the passive cooling and passive heating zones' way of variating, where the ranges are pushed towards their potential limits. Results have also demonstrated that if future weather data is not included in simulations, a weather-related performance gap is generated.
... Possible future research would focus on the analysis of user behaviour in heating consumption. Jimenez-Bescos et al. [30] concluded that incorporating user behaviour into building simulations, a more accurate estimation of energy consumption could be achieved. ...
The energy poverty derived from socio-economic imbalances affects mostly households with fewer economic resources, being social housing complexes one of the most vulnerable sectors. The insufficient access to energy and the incapability to maintain dwellings at an adequate temperature can have negative impact on people’s health due to the prolonged exposure to poor hygrothermal conditions. Therefore, the prioritization of building retrofitting actions must be carried out regarding the actual state of the housing and the family economy. This paper proposes the definition of a prioritization map that gave a general knowledge of the energy vulnerability situation of the existing building stock. To this end, the dwelling’s energy performance is analysed, focusing on the correlation among its characteristics and the energy vulnerability of its inhabitants. In this way, dwellings with high energy poverty potential are identified in order to develop different energy retrofitting strategies. By applying this method to 14 case studies of social housing in Bilbao, Spain, it was obtained a prioritization map with six levels of vulnerability that can serve as a tool for public entities to design their future strategies. It has been proven that building compactness and year of construction are important factors with a great impact on the heating demand and final consumption in dwellings. Acknowledging the vulnerability context of the building stock eases the decision-making process and the definition of intervention guidelines, prioritizing those in a situation of greater vulnerability.
... For instance, in the residential sector, the occupants behaviour influence the overall building energy consumption as the occupants in energy-efficient buildings should consider natural methods of ventilation by adapting to changes in the weather instead of being reliant on mechanical systems of heating and cooling. So for building the designers have some responsibilities in training the occupants of how to use their future home using, for example, a simple brochure/ manual illustrating when and how to apply the main energy-efficient techniques inside the building [29]- [32]. ...
Energy efficiency is one of the most widely used tools for both energy saving and environmental protection against greenhouse gases. Some energy efficiency techniques are being used to reduce energy consumption. This research focuses on optimising the relation of time and energy, where the best scenario of energy-saving for specified applications will be considered with the time required in achieving these scenarios. To implement this, we adopt two engineering applications (car and water pumps) on each application, with specific constraints and parameters to test the time energy relation. It was being found that for both applications, there is an optimum engineering scenario where the least amount of energy (using the extra time to minimise energy consumption) can be achieved while the remaining cases will consume higher energy. For instance, for a specific type of car used in this study, the optimum car speed was found to be between 65–70 km h– ¹ ; at this speed, the car consumes the least amount of energy (around 137 MJ when travelling a distance of 100 km). All the speeds less than the optimum speed will consume more energy; the same is true when the speed is increased over the optimum. For the second application using water pumps, it was found that a 1.1 kW pump is the most efficient at pumping a specific amount of water, and using higher or lower rated pumps will consume higher energy levels but correspondingly will reduce the time required to perform the same application. This research emphasised the concept that time can save energy, which is not yet covered in the literature as time value of energy when time is not an essential aspect and can be delayed without affecting the main tasks.
... User behaviour has a non-negligible importance for energy consumption. Today, even building simulations aim to take user behaviour into account [2]. Interventions to change energy use behaviour have the potential to help reduce energy demand, avoid greenhouse gas emissions and reduce energy costs. ...
Non-residential buildings in the European Union consume more than one third of the building sector’s total. Many non-residential buildings are owned by municipalities. This paper reports about an energy saving competition that was carried out in 91 municipal buildings in eight EU member states in 2019. For each public building an energy team was formed. The energy teams’ activities encompassed motivating changes in the energy use behaviour of employees and small investments. Two challenges added an element of gamification to the energy saving competition. To assess the success of the energy saving competition, an energy performance baseline was calculated using energy consumption data of each public building from previous years. Energy consumption in the competition year was monitored on a monthly base. After the competition the top energy savers from each country were determined by the percentage-based reduction of energy consumption compared to the baseline. On average, the buildings had an electricity and heat consumption in 2019 that was about 8 % and 7 %, respectively, lower than the baseline. As an additional data source for the evaluation, a survey among energy team members was conducted at the beginning and after the energy competition. Support from superiors, employee interest and motivation and behaviour change as assessed by energy team members show a positive, if weak or moderate, correlation with changes in electricity consumption, but not with changes in heat consumption.
... The aim of this study was to evaluate the effect of different base temperatures and their impact on the correlation between energy consumption and degree-days. The base temperature was selected as the standard 15 degree centigrade for the region [12], the balance temperature calculated with dynamic building simulations and the thermostat temperature setting as collected by questionnaires [18]. ...
... The properties are a combination of flats and houses, from different construction periods, tenancies, occupancy and sizes. Details of the properties were presented by the authors in [18] containing all the specific parameters regarding the properties. ...
... The bills relate to gas natural for the use of space heating and domestic hot water. Furthermore, the thermostat setting for each property was collected by a questionnaire, as presented in Jimenez-Bescos & Oregi [18], and this setting will be used as the thermostat temperature. ...
Degree-days are used as a forecasting tool to predict energy demand and for normalizing energy consumption to be able to compare between different properties across different years. The base temperature is the main aspect to accurately calculate degree-days. The aim of this study was to evaluate the effect of different base temperatures and their impact on the correlation between energy consumption and degree-days. The base temperature was selected as the standard 15 °C for the region, the balance temperature calculated with dynamic building simulations and the thermostat temperature setting as collected by questionnaires. The methodology followed is based on the analysis of 20 properties located in the cities of Bilbao, San Sebastian and Vitoria in northern Spain. The properties are a combination of flats and houses, from different construction periods, tenancies, occupancy and sizes. This study had highlighted the effect and impact of selecting different base temperatures for the calculation of degree-days and the correlation between energy consumption and degree-days. While the use of the balance temperature as base temperature could generate very good correlation, they were not so dissimilar from using the standard 15 °C base temperature to justify the amount of extra work required to generate the balance temperature. The use of the thermostat setting as an indication of the base temperature was not as reliable as the other base temperature methods in generating a good correlation to explain the energy consumption on the 20 properties investigated in this study.
... Another change could be focus on inhabitants in rural areas of the country, as the income level and information distribution there is different, which might lead to different results regarding public awareness regarding air pollution and flue gas treatment options. Different aspects to be considered in the future research could be user behaviour regarding thermal energy consumption in households to identify possible improvements in combustion equipment performance [21]. ...
The paper displays results of the questionnaire called “Particulate matter pollution in air”, which serves as a tool to determine level of public awareness of the health risks related to pollution from small capacity heating equipment in households. Barriers for installation of the innovative flue gas treatment technology called a fog unit in households and possible mechanisms to decrease or prevent these barriers were defined. The first part of the questionnaire included overall information about participants: age, gender, education level, place of residence, activities to protect the environment and motives behind performing these activities. The remaining questions were divided in four groups: “Environmental views”, “Knowledge on air pollution”, “Willingness to pay”, “Choice of flue gas treatment technologies”. The results of questionnaire correspond with raised problem situations. Over 80 % of respondents lack information on pollution and possible consequences deriving from it, and on potential solutions to prevent pollution. Residents of households are willing to pay for installation of flue gas treatment equipment (capital investments).
