Building Energy Need Research Articles

Effect of Roof Inclination on Solar Panel’s Energy Output in a South-Western Nigerian City

This work sets up four photovoltaic (PV)-based solar power systems with adjustable solar panel inclination angle stands. It also varies the inclination angle of the solar panel every five days while recording daily energy delivered by the panels. The study finally compares the output energy recorded for different angles. This was done with a view to studying the effect of building roof inclination on the energy deliverable by solar panel installed on it. The set up consists of PV-based solar power systems with adjustable inclination angle stands for four 10 W solar panels which are on the same axis. Each solar panel was connected to a 42 Ah battery through multimeter and charge controller. A 25 W load was used to discharge the battery at night to allow fresh charge from the panels during the day. The multimeter was used to monitor the daily energy delivered. The solar panels were subjected to 0°, 25°, 50° and 75° inclination angles and faced to South direction at the Renewable Energy Laboratory, Osun State University, Osogbo, Nigeria. Data were recorded daily for 20 days by subjecting all panels to the considered angles for five days. The results obtained showed that the average energy delivered by panels installed at the stated inclination angles are 35.35 Wh, 32.25 Wh, 24.1 Wh and 13.95 Wh, respectively. This means that, for a specific building energy need, the steeper the roof (intended for panel installation), the more the amount of solar panel required to meet such energy. The results also showed various expressions that can be used to estimate daily average sun hour (ASH) based on roof inclination. For example, the estimated daily (ASH) for Osogbo and environs can be taken to be 4.22 hour, 3.15 hour and 1.82 hour for 25°, 50° and 75° roof inclinations, respectively.

Assessments regarding the performances of wind turbines from the roofs of buildings

In the current energy context, wind energy remains the main renewable energy source. Greater attention should be paid to small-sized wind turbines, mounted on the roofs of buildings, for their many advantages: easy to construct, do not require additional space, easy maintenance, eliminate losses from the system and transport costs, can provide alone or in addition to energy solar, the entire energy need of buildings, etc. The present paper analyzes three models of wind turbines that can be placed on the roofs of buildings: crossflow wind turbine Banki, ridge blade turbine and aeroMINE model. The analysis refers to their constructive solutions, the working principle, the optimal design for a good power coefficient and the energy that can be obtained with these small turbines for urban applications and not only.

Privacy-Preserving Context-Based Electric Vehicle Dispatching for Energy Scheduling in Microgrids: An Online Learning Approach

Electric Vehicles (EVs) are beginning to play a key role in the fast developing area of Internet of Things (IoT). Numerous results have shown the feasibility of vehicle-to-building (V2B) mode of charge/discharge, where EVs are considered as dynamically configurable dispersed energy storage units. When properly incorporated into the building energy system, EVs are able to provide ancillary services to the power grid during high demand periods or outage situations. The arising challenge is how to act intelligent behaviors in complex and changing microgrid environments. With the aim of minimizing the cost and maximizing satisfaction degree, this paper, unlike previous works, jointly considers the building energy need and the safety/willingness of EVs to find and dispatch the optimal vehicle to conduct auxiliary or supportive actions. To realize that, we propose an intelligent Privacy-preserving Context-based Online EV Dispatching System (PCOEDS), using a tree-based structure which supports the ever-increasing big metering datasets with context-awareness. Moreover, privacy preservation and security protection on both sides of the the energy transmission process are well guaranteed in our work. Theoretical results validate that our intelligent dispatching system achieves sublinear regret and differential privacy, which outperforms other online learning method when applied to a huge city-level dataset.

An experimental method for building energy need evaluation at real operative conditions. A case study validation

Building energy consumption reduction is fundamental to achieve sustainable green design and environmental impact lowering. Thermo-physics, different uses over time, heating/cooling plant and controlled mechanical ventilation system operation and users’ behaviour in the building affects energy saving conditions, indoor environmental quality and people wellbeing. In our research an experimental method for building energy performance evaluation under transient conditions was proposed. Monitoring experimental data of the external and internal microclimate, and of heat flux through each wall, under real dynamic conditions, were used for a real-time post-processing. The proposed method was based on the “modified” degree days calculated by the measured climatic data, taking into account the thermal phase shift and thermal capacity of the various construction materials. An experimental monitoring campaign was carried out for three years on a social housing development in Florence, as the case study. Results assessment provided a linear correlation function between the above mentioned parameters, with a coefficient of determination higher than 80%. The proposed method can be used for building energy consumption prediction that is fundamental for guaranteeing wellbeing, health and energy sustainability with the “green” transition from energy consuming community and buildings to energy producing community and buildings.

Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need

Accurate prediction of building energy need plays a fundamental role in building design, despite the high computational cost to search for optimal energy saving solutions. An important advancement in the reduction of computational time could come from the application of machine learning models to circumvent energy simulations. With the goal of drastically limiting the number of simulations, in this paper we investigate the regression performance of different machine learning models, i.e., Support Vector Machine, Random Forest, and Extreme Gradient Boosting, trained on a small data-set of energy simulations performed on a case study building. Among the XX algorithms, the tree-based Extreme Gradient Boosting showed the best performance. Overall, we find that machine learning methods offer efficient and interpretable solutions, that could help academics and professionals in shaping better design strategies, informed by feature importance.

Power Management of Solar PV Systems for PEER Load

The demand for integration of solar-based photovoltaic (PV) system to meet the energy need of residential buildings is growing very rapidly. However, the energy management system of these buildings has become an emerging area of research due to the rapid implementation of PV systems. The objective of this article is to propose an intelligent controller to share power generation of two PV sources for energy supply in buildings. The control is implemented using an adaptive neuro fuzzy inference system. The proposed methodology is developed with two different rooftop PV systems to achieve sharing of generated power as per the load profiles. The proposed design has better utilization of PV system installed in a residential area, and the results depict the maximum utilization of solar power.

Proposal of a new automated workflow for the computational performance-driven design optimization of building energy need and construction cost

Delaying the analysis of the building performance to the final stages of the design process can often cause the actual results to differ from the expected ones, with the issue of significant redesign costs. The aim of this work is to improve the designer’s control on thermal and geometric variables of the building in the first stages of the design process, to obtain an optimized building from different points of view. An innovative computational performance-driven design optimization workflow is proposed. The integration of a parametric algorithmic modelling tool in a genetic algorithm powered performance optimization procedure allows to minimize energy need and construction cost of the building, and to optimize the natural illumination levels, affecting several geometric variables. This approach is tested on the early design stages for the new construction of an educational building located in the centre of Italy (L’Aquila). The results are post-processed in order to provide an innovative graphical visualization of the outputs to improve the decision-making process. Useful insights on the influence of different design choices on three key performance indicators can be drawn for the case study building, by comparison of the Pareto solutions: optimization results highlight the strongest influence of window-to-wall ratio, among the other variables, on energy need and useful daylight illuminance.

Development of a Reduced Order Model of Solar Heat Gains Prediction

The aim of this study was to elaborate and validate a reduced order model able to forecast solar heat gains as a function of the architectural parameters that determine the solar heat gains. The study focused on office buildings in Catalonia and Spain and their physical values were taken from the Spanish Building Technical Code and European Union Directive 2018/844. A reduced order model with three direct variables (solar heat gain coefficient, shade factor, window to wall ratio) and one indirect design variable (building orientation) was obtained and validated in respect to the International Performance Measurement and Verification Protocol. Building envelope properties were fixed and the values were taken from the national standards of Spain. This work validates solar heat gain coefficient as a primary variable in determining the annual solar heat gains in a building. Further work of developed model could result in building energy need quick evaluation tool in terms of solar heat gains for architects in building early stage as it has an advantage over detailed building simulation programs in terms of instant calculation and the limited need for predefined input data.

Human-centric green building design: the energy saving potential of occupants’ behaviour enhancement in the office environment

Buildings energy efficiency is highly dependent on occupants’ energy and environmental lifestyle. In this view, this work aims at identifying the potential benefits of human-based energy retrofit strategies, namely human behaviour triggering actions and associated energy awareness, through large-scale surveys and calibrated building dynamic simulation. In detail, a questionnaire is submitted to office occupants to understand workers’ energy behaviours, indoor environmental perception, and identify tailored triggering actions. Therefore, different occupant behaviour scenarios involving energy wasteful and efficient human-building interactions are modelled when implemented in a case study office building in Italy. Findings show that the elimination of energy wasteful behaviours may involve building energy need reduction up to 17%. Furthermore, triggers aimed at driving energy efficient human control of HVAC setting and natural ventilation provide further annual performance improvement. Therefore, motivating occupants to change behaviour is a challenge to reduce energy consumption while improving indoor environmental conditions and cost-energy benefits.

Intra-urban microclimate mapping for citizens’ wellbeing: Novel wearable sensing techniques and automatized data-processing

Cities alter their-own climate due to their configuration and land surface thermal properties. The main effect is the well-known phenomenon of the Urban Heat Island (UHI) meaning higher temperatures experienced in urbanized environment with respect to rural surroundings. Nevertheless, thermal behavior of cities varies even within the city itself and the investigation of intra-urban microclimate diversification is of extreme importance in detecting the most critical conditions for citizens well-being and real building energy need estimation. This work wants to map the urban microclimate environment by means of a miniaturized and wearable weather station designed by the authors to catch the pedestrian perspective in the outdoors. Collected data are processed in order to erase elapsed-time-dependency of the observations collected through the mobile monitoring system and manual and automated data clustering procedure are compared to assess potential of the proposed automatized procedure in detecting peculiarities of the urban structure. According to results, the system demonstrates to catch the intra-urban microclimate diversification and the proposed data processing procedure gives back reliable results in terms of direct comparison of site specific environmental data and phisical drivers. Finally, the automatized clustering process produces better results for day-time monitoring in compact areas assuming temperature, relative humidity and solar radiation as environmental drivers.

Sensitivity Analysis of the Thermal Energy Need of a Residential Building Assessed by means of the EN ISO 52016 Simplified Dynamic Method

The EN ISO 52016-1:2018 technical standard has introduced a new simplified dynamic method for the calculation of the building energy need for heating and cooling. This new procedure combines a low amount of input data required, as for the previous quasi-steady and dynamic simplified methods of the withdrawn EN ISO 13790 standard, with an increased accuracy, which would reduce the gap with detailed dynamic methods. This work is part of a broader research activity aimed at investigating the new simplified dynamic model and highlighting its strengths and weaknesses, in terms of accuracy and robustness. Specifically, the work addresses the parameters that have a great influence on the final results and the effects of uncertainties in input data. To this purpose both standard and tailored energy performance assessments have been applied, in particular in the first one a continuous operation period of the space heating system was supposed, and in the second one an intermittent operation system was chosen. A sensitivity analysis was also carried out to quantify the variation of the heating and cooling loads with the set-point temperature, the windows physical properties, the heat capacity and the thermal transmission properties of opaque components, as well as the occupancy related input parameters, such as the internal heat gains and the ventilation flow rate. The analysis was applied to a multi-unit residential building located in Rome and built in the first half of the 20th century. The results outline absolute relevance of the set point temperatures. The significance of occupant behaviour and the importance of the correct definition of the component thermal properties is also pointed out through the comparison between the standard and tailored assessments.

Resilience of a Building to Future Climate Conditions in Three European Cities

Building energy need simulations are usually performed using input files that contain information about the averaged weather data based on historical patterns. Therefore, the simulations performed are not able to provide information about possible future scenarios due to climate change. In this work, future trends of building energy demands due to the climate change across Europe were studied by comparing three time steps (present, 2050, and -2080) in three different European cities, characterized by different Köppen-Geiger climatic classes. A residential building with modern architectural features was taken into consideration for the simulations. Future climate conditions were reached by applying the effects of climate changes to current hourly meteorological data though the climate change tool world weather file generator (CCWorldWeatherGen) tool, according to the guidelines established by the Intergovernmental Panel on Climate Change. In order to examine the resilience of the building, the simulations carried out were compared with respect to: peak power, median values of the power, and energy consumed by heating and cooling system. The observed trend shows a general reduction in the energy needs for heating (–46% for Aberdeen, –80% for Palermo, –36% for Prague in 2080 compared to the present) and increase (occurrence for Aberdeen) in cooling requirements. These results imply a revaluation of system size.

Cool Roof Impact on Building Energy Need: The Role of Thermal Insulation with Varying Climate Conditions

Cool roof effectiveness in improving building thermal-energy performance is affected by different variables. In particular, roof insulation level and climate conditions are key parameters influencing cool roofs benefits and whole building energy performance. This work aims at assessing the role of cool roof in the optimum roof configuration, i.e., combination of solar reflectance capability and thermal insulation level, in terms of building energy performance in different climate conditions worldwide. To this aim, coupled dynamic thermal-energy simulation and optimization analysis is carried out. In detail, multi-dimensional optimization of combined building roof thermal insulation and solar reflectance is developed to minimize building annual energy consumption for heating–cooling. Results highlight how a high reflectance roof minimizes annual energy need for a small standard office building in the majority of considered climates. Moreover, building energy performance is more sensitive to roof solar reflectance than thermal insulation level, except for the coldest conditions. Therefore, for the selected building, the optimum roof typology presents high solar reflectance capability (0.8) and no/low insulation level (0.00–0.03 m), except for extremely hot or cold climate zones. Accordingly, this research shows how the classic approach of super-insulated buildings should be reframed for the office case toward truly environmentally friendly buildings.

A data-driven procedure to model occupancy and occupant-related electric load profiles in residential buildings for energy simulation

Improving the reliability of energy simulation outputs is becoming a pressing task to reduce the performance gap between the design and the operation of buildings. Occupant behaviour modelling is one of the most relevant sources of uncertainty in building energy modelling and is typically modelled via a priori choices made by modellers. Thus, an improvement in the description of occupant behaviour is needed. To this regard, the availability of smart meter recordings might help to generate more reliable input data for building energy models. This paper discusses a novel data-driven procedure that enables to create yearly occupancy and occupant-related electric load profiles to inform building energy modelling, using a typical uneven database made available by energy operators. The procedure is subdivided into three main tasks. The first has the intent to detect representative occupant-related electric load profiles from smart meters readings. The second task aims to generate yearly occupancy profiles from the same database. The last task assesses the impact of the generated occupancy and occupant-related electric load profiles on building energy simulation outputs. The procedure is applied to the case study of a multi-residential building in Milan, Italy and is meant to show the possibility to overcome deterministic inputs that might have little relation with the actual building operation. It showed a substantial improvement in the reliability of building energy simulation and that occupant related load profiles may account for about 8% of the building's energy need for space heating.

A Simplified Methodology for Existing Tertiary Buildings’ Cooling Energy Need Estimation at District Level: A Feasibility Study of a District Cooling System in Marrakech

In district energy systems planning, the calculation of energy needs is a crucial step in making the investment profitable. Although several computational approaches exist for estimating the thermal energy need of individual buildings, this is challenging at the district level due to the amount of data needed, the diversity of building types, and the uncertainty of connections. The aim of this paper is to present a simplified measurement-based methodology for estimating the cooling energy needs at the district level, which can be employed in the preliminary sizing and design of a district cooling network. The methodology proposed is suitable for tertiary buildings and is based on building electricity bills as historical data to calculate the yearly cooling demand. Then, the developed method is applied to a real case study: the feasibility analysis of a sustainable district cooling network for a hotel district in the city of Marrakech. The designed system foresees a 23-MWcold district cooling network that is 4 km long, supplying 26 GWh of cooling to the tourist area. The results show that the proposed methodology for cooling demand estimation is coherent with the other existing methods in the literature.

The influence of thermal bridge calculation method on the building energy need: a case study

The goal of the paper is to compare two different methods of calculating the linear transmittance of thermal bridges in order to evaluate the building energy need for heating. As a case study, it was considered an existing non-isolated residential building of ‘70 years consisting of 30 housing units. All the construction details of the building are known. The energy analysis of the building was carried out using a commercial software. The linear transmittance of the thermal bridges was determined both by the catalogue, provided by the software itself, and by the numerical finite-element evaluation according to UNI EN ISO 10211 standard using a 2-D numerical simulator. Through numerical analysis it is possible to evaluate in a detailed way all the thermal bridges present in the building and therefore it is possible to evaluate the approximation induced by the use of the catalogue. Results show that the detailed analysis leads to a transmission heat exchange through thermal bridges about eight times greater than that estimated through the catalogue and consequently to a higher building energy need of about 12%. The heating energy needs per unit area of the individual housing units were also compared.

How outdoor microclimate mitigation affects building thermal-energy performance: A new design-stage method for energy saving in residential near-zero energy settlements in Italy

Abstract Key research effort was dedicated toward zero-energy buildings. Recent interest is switching from single-building to inter-building scale, to enhance the whole district economic-environmental sustainability. This upscaling opens the doors to further optimization strategies thanks to outdoor microclimate mitigation aimed at reducing winter thermal losses and summer overheating. This work proposes a novel design-stage replicable method for multiscale microclimate improvement correlated to building thermal-energy analysis in a residential near-zero-energy district in central Italy. Cool surface installation and conscious greenery design in the outdoors were used in the district of single-family houses. Therefore, a microclimate simulation was performed to elaborate realistic weather files to be used in the second-stage dynamic thermal-energy simulation at building level, in order to evaluate the impact of the local microclimate on building thermal-energy performance and renewables’ production. Microclimate analysis demonstrated how local conditions affect outdoor comfort and that they may be considered in the same way as traditional energy-efficiency improvement early-stage design strategies at building scale, being able to reduce building energy need for HVAC up to 10% in the studied conditions. Therefore, new potentialities of inter-building scale analyses for energy efficiency enhancement and renewable energy exploitation are expected while downscaling microclimate assessment toward building thermal-energy performance.

Comparison among Detailed and Simplified Calculation Methods for Thermal and Energy Assessment of the Building Envelope and the Shadings of a New Wooden nZEB House

This paper deals with research carried out by the University of Florence on the thermal and energy performances of a recently built nZEB in Mediterranean Italian area. Heterogeneous component and thermal bridges performances have been analysed and critically evaluated with different calculation methods, and the results in terms of energy consumptions for heating and cooling have been compared. Some solar shading devices have been evaluated to reduce the building energy need for cooling. Main results of the research are presented for the components and thermal bridges properties and for the energy balance of the building implemented with different solar shadings.

Influence of Comfort Expectations on Building Energy Need

Increase thermal comfort is considered as one of the main benefits of a deep renovation right after energy saving. However, an increase in thermal comfort could be seen as a behavioural change caused by the energy efficiency improvement that reduces expected energy saving: the so-called rebound effect.This paper shows how building energy need is correlated to comfort category, defined in EN 15251. The study is conducted via dynamic simulations performed by TRNSYS 17 software using 3D multi-zone models. Models are tailored on occupant behaviour and driven by thermal comfort constraints. Calculations of energy need for space heating and space cooling is done both before and after a deep renovation. The effect of building and user characteristics is evaluated too. Users are differentiated by number of persons and occupancy schedule.The relation between thermal comfort, set-point temperature and energy need is investigated, focusing attention on changes that occur after the building has been thermally insulated. Computational results are critically discussed and compared with an empirical study on building renovation that includes a survey on thermal comfort perception and user behaviour. Finally, rebound effect is discussed and its magnitude is evaluated.

CITYBEM: AN OPEN SOURCE IMPLEMENTATION AND VALIDATION OF MONTHLY HEATING AND COOLING ENERGY NEEDS FOR 3D BUILDINGS IN CITIES

Abstract. Cities play an important role in reaching local and global targets on energy efficiency and the reduction of greenhouse gas emissions. In order to determine the potential of energy efficiency in the building sector new planning instruments are required that allow depicting the complete building stock on the one hand and investigate detailed measures on the other hand. To pursue this objective, the ISO 13970:2008 monthly heating and cooling energy model is implemented using an open source based software architecture (CityBEM), in connection with data from 3D city models in the CityGML standard (LOD2). Input parameters such as the building geometry, typology and energy characteristics have been associated with the 3D data. The model has been applied to several urban districts with different numbers of buildings in the city of Karlsruhe. In order to test the accuracy of the implemented model and its robustness, a 3-step validation has been conducted. The comparison of simulation results with results based on a TRNSYS simulation showed acceptable results for the studied application cases. The proposed approach can help urban decision makers to perform a city or district wide analysis of the building energy need which can be further used to prepare future scenarios or renovation plans to support decision making.