Building Energy Demand Research Articles

Exploring the Effects of Climate-Adaptive Building Shells: An Applicative Time-Saving Algorithm on a Case Study in Bologna, Italy

Adaptive façades represent a viable and effective technological solution to reduce the building energy demand for cooling while achieving interesting aesthetic effects on the building envelope to screen solar radiation. During the last decade, many different design solutions, including those based on shape memory alloys, have been experimented to obtain appropriate responses without being dependent on electro-mechanically actuated systems. Several recent and ongoing studies have been published in the scientific literature regarding the different actuator typologies, as well as the different properties of the materials used, which usually determine the adaptive solution characteristics after a series of complex and time-consuming simulations using specialised dynamic modelling software. Due to the time and resources required, this kind of evaluation is usually delivered during the last and more advanced design stage as a form of assessment of already-taken architectural and technological choices. The study reported in the paper aims to offer a quick, time-saving simplified algorithm to calculate the response of an adaptive façade, according to the ISO 13790 standards, to be adopted during the early design stage to evaluate the possible effects of design decisions. The study includes three main steps: (a) the conceptualisation of the adaptive solution considering the context conditions; (b) the definition of the calculation algorithm; (c) the application of the method to a test room in a case study building located in Bologna for supporting the discussion of the related outcomes.

Weighted aggregated ensemble model for energy demand management of buildings

Accurate building energy consumption prediction is essential for achieving energy savings and boosting the HVAC system's efficiency of operations. Therefore, in this work, a novel ensemble predictive model, which combines the weighted linear aggregation of Gaussian process regression (GPR) and least squared boosted regression trees (LSB), leading to WGPRLSB, is proposed for the accurate estimation of energy usage in the cases of Heating Load (HL) and Cooling Load (CL). Marine predator optimization (MPO) is used to evaluate the optimal values of the design parameters of the proposed methodology. Further, predictive models based on linear regression (LR), support vector regression (SVR), multilayer perceptron neural network (MLPNN), decision tree (DT), and generalized additive model (GAM) are also designed for comparison purposes. The results reveal that the value of RMSE is reduced by 12.4%–70.7% (HL) and 39.7%–64.9% (CL) for WGPRLSB in comparison to the other predictive models. The results of the performance index (PI) also confirm the effectiveness of the proposed model energy consumption prediction for HL and CL. Furthermore, the performance investigation on the second dataset reveals that WGPRLSB achieves the highest value of VAF (97.20%) compared to other designed models. It may be concluded that the proposed WGPRLSB accurately forecasts building energy demands.

Building a Sustainable Energy Community: Design and Integrate Variable Renewable Energy Systems for Rural Communities

This study proposes a decentralized hybrid energy system consisting of solar photovoltaics (PV) and wind turbines (WT) connected with the local power grid for a small Najran, Saudi Arabia community. The goal is to provide the selected community with sustainable energy to cover a partial load of the residential buildings and the power requirements for irrigation. For this, a dynamic model was constructed to estimate the hourly energy demand for residential buildings consisting of 20 apartments with a total floor area of 4640 m2, and the energy requirements for irrigation to supply a farm of 10,000 m2 with water. Subsequently, HOMER software was used to optimize the proposed hybrid energy system. Even considering the hourly fluctuations of renewable energies, the artificial neural network (ANN) successfully estimated PV and wind energy. Based on the mathematical calculations, the final R-square values were 0.928 and 0.993 for PV and wind energy, respectively. According to the findings, the cost of energy (COE) for the optimized hybrid energy system is $0.1053/kWh with a renewable energy penetration of 65%. In addition, the proposed system will save 233 tons of greenhouse gases annually.

Innovative numerical procedure for simulating borehole heat exchangers operation and interpreting thermal response test through MODFLOW-USG code

In recent years, among renewable energies, the geothermal resource exploitation shows a constant growth; specifically, in countries engaged in CO2 emissions reduction and dependent on energy from abroad, the low-temperature geothermal energy (geo-exchange) for air conditioning of buildings represents a cost-effective and green solution. In closed-loop systems borehole heat exchangers (BHE) are coupled with ground-source heat pumps (GSHP) constituting the key component of the heating ventilation air-conditioning (HVAC) system. Therefore, the design of the BHE and the correct interpretation of in situ Thermal Response Tests (TRT) are essential to supply the building energy demand. To support the design, Modflow-USG Connected Linear Network (CLN) and Drain Return Flow (DRT) packages are adapted and improved to reproduce the operation of one or more BHE in aquifers and to analyze the TRT. The improvements are compared with a previously developed numerical model and two different analytical solutions (infinite line source and moving line source) by imposing a constant heat rate injection into the aquifer. The results show good agreement between the new approach and previous ones (discrepancy lower than 2% for models with highly refined grid), but the new approach is much more accurate and expeditious in both implementation and execution, also allowing for an easy numerical simulation of multiple BHE.

Determination of Rational Values of the Parameters of an Unventilated Trombe Wall Using the Method of Multicriteria Optimization for the Climatic Conditions of Uzbekistan

The energy demands of the construction sector are expected to multiply over the next decade, driven by population growth, rising incomes, accelerating urbanization and changes in consumption patterns. This process is also dramatically affected by the depletion of fossil resources and high specific energy consumption for heating buildings. Under these conditions, it is necessary to develop comprehensive energy efficiency measures in these areas to meet the energy demands of residential, commercial and administrative buildings. In this work, using the methods of degree-day, multicriteria optimization and regression analysis, the optimal combinations of factors for an unventilated Trombe wall are determined: the orientation of the building, the thermal support of the translucent enclosure of the unventilated Trombe wall and the ratio of the surface of the unventilated Trombe wall to the surface of the building facade. The calculations were performed for three levels of thermal protection for buildings in the climatic conditions of the city of Tashkent (Uzbekistan). A typical one-storey three-room house was chosen as the object. As the calculation results show, within the considered range of values, the relative dominances of the factors are as follows: orientation - 5.37%; thermal support of translucent barriers - 72.95%; and area ratio - 21.68%. Using the optimal values, the specific energy consumption of buildings for heating can be reduced from an average of 12.9-14.8% to 52.6-65.3%. Additionally, the CO2 emissions are reduced from 5621.8 kg to 12435.5 kg per year. The discounted payback period, depending on the investment, ranges from 18.7 to 40.9 years. Regression equations are proposed for three levels of thermal protection of the considered object, making it possible to determine the specific energy consumption for heating.

Multivariate analysis for assessing the thermal performance of vertical opaque envelopes in extended regions

We introduce a statistical methodology to evaluate the thermal performance of vertical opaque envelopes and provide the most adequate design of energy-efficient buildings located across extended regions. The analytical procedure was applied to the extensive Argentinian territory with a variety of climates and a limited number of networked meteorological stations. Although the study was conducted over a full year, results are presented for January and June, when the building energy demand for heating and cooling is most significant, taking into account the local climate, the thermal properties of the walls and the effects of the daily variation in the solar radiation. By using the Fourier series expansion of the sol-air temperature and multivariate analysis, we first correlated the weather data and the steady-state and time-dependent heat fluxes transmitted by conduction through five types of typical walls facing north and south in 10 climatically differentiated cities where full weather data were recorded. Then, the mean values of the sol-air temperature and the amplitude of its time variations were interpolated throughout the territory, thus yielding the spatial distributions of these parameters for a typical day in the months of interest. Finally, the calculation of the heat fluxes exchanged through building opaque envelopes was extended to the whole country.

Energy Simulations of a Building Insulated with a Hemp-Lime Composite with Different Wall and Node Variants

Thermal bridges constitute a significant share in the overall heat losses through building partitions. This is an important issue not only in traditional but also ecological buildings, where the load-bearing structure is often a wooden frame. In partitions insulated with hemp-lime composite, the skeleton is usually hidden in the insulation. However, in some nodes or jambs, wooden elements may be exposed or have a large cross-section, intensifying the heat transfer. This work presents simulations of energy demand in a single-family building insulated with hemp-lime composite, using the BSim dynamic simulation program. The calculations take into account the linear thermal transmittance of structural nodes modeled in the THERM program. The energy demand for heating and the share of thermal bridges in the heat loss of the entire building were calculated for different locations of the structural framework in the walls, as well as the size and number of windows. The share of thermal bridges in heat losses was about 10%, and the differences in energy demand for heating using various frame locations in the wall were negligible.

Optimization of energy transmittance through building envelope for hot dry climate

Fenestrations of the buildings are playing an important role in the building’s energy efficiency for the tropical climate. The energy that comes from the glass window into the building can be restricted by providing shading on the windows. So, to provide the shading on the window, the most common way is fixed shade like overhang and fin. Shading devices are very helpful to control the SHGC and light transmittance through the fenestrations of the building. Most of the studies analyze the impact of WWR, shadings, and SHGC on a building’s energy demand but did not consider the combined effect of these parameters. By considering all the parameters of the building envelope in the analysis like WWR, orientation, shading devices, projection factor, and type of glass, the energy demand in the building can be significantly reduced. The goal of this paper is to analyze the variation of residential envelope transmittance value with the window-wall ratio, projection factor, and fixed shading devices and also to optimize the energy from building envelope elements in residential buildings using a statistical method, namely the Taguchi method. The transmittance of energy through the building envelope is calculated in 8 cardinal directions by providing shading with the fin and overhang. Based on the result, the optimum energy saving achieved by selecting the optimum combination of the process parameters for minimizing the energy from the building envelope is A1B1C3D2 i.e. window to wall ratio 7%, triple glazing type of glass used, shading with the overhang fin, and orientation in the west direction. The result shows the most significant factor among the selected parameter is the window to wall ratio. Also, by focusing on the building envelope parameters, an architectural designer may reduce the building energy demand significantly and offer more alternatives to achieve energy-efficient buildings.

Net Positive Energy Buildings in Architectural Context

Considering the rapid depletion of natural resources because of increasing comfort conditions in living spaces and increasing expectations from buildings due to developing technology and climate change, today it has become inevitable to develop effective policies to meet the energy demand of buildings and to create renewable and sustainable systems. In this context, new generation building design in architecture requires regulations that reduce energy consumption, protect natural resources while considering climatic conditions and carbon emissions. In this study, buildings with net-positive energy were evaluated with the comparative analysis method and inferences were made for new generation building designs. In the first step, the properties of buildings with net-positive energy are discussed. Then, it is aimed to evaluate the buildings in which the design principles determined for a positive energy building are applied according to these features. In this context, ten buildings, which can produce more energy than the energy they consume and whose designs were made by considering the energy criteria developed according to different climatic regions, were examined. It is aimed the outcomes of this study to raise awareness against building design concepts with net-positive energy and contribute to raise the number of buildings designed with energy concepts.

Deep reinforcement learning with planning guardrails for building energy demand response

Building energy demand response is projected to be important in decarbonizing energy use. A demand response program that communicates “artificial” hourly price signals to workers as part of a social game has the potential to elicit energy consumption changes that simultaneously reduce energy costs and emissions. The efficacy of such a program depends on the pricing agent’s ability to learn how workers respond to prices and mitigate the risk of high energy costs during this learning process. We assess the value of deep reinforcement learning (RL) for mitigating this risk. Specifically, we explore the value of combining: (i) a model-free RL method that can learn by posting price signals to workers, (ii) a supervisory “planning model” that provides a synthetic learning environment, and (iii) a guardrail method that determines whether a price should be posted to real workers or the planning environment for feedback. In a simulated medium-sized office building, we compare our pricing agent against existing model-free and model-based deep RL agents, and the simpler strategy of passing on the time-of-use price signal to workers. We find that our controller eliminates 175,000 US Dollars in initial investment, decreases by 30% the energy cost, and curbs emissions by 32% compared to energy consumption under the time-of-use rate. In contrast, the model-free and model-based deep RL benchmarks are unable to overcome initial learning costs. Our results bode well for risk-aware deep RL facilitating the deployment of building demand response.

A multilevel window state model based on outdoor environmental conditions that captures behavioural variation at room and apartment levels

Occupants’ use of windows can influence the building energy demand, thermal conditions and indoor air quality. Researchers have made substantial efforts to develop probabilistic models to predict the window open/closed state. However, the hierarchical data structure and the heterogeneity in occupant behaviour have been generally neglected in previous modelling efforts. Multilevel modelling can provide an appropriate framework to handle this type of data structure and variability, but this method has rarely been used in the field. This study investigated room- and apartment-level variations in the effects of outdoor environmental variables on the window open state in low-energy apartment buildings in the UK using a multilevel modelling approach. The results showed that the room-level, rather than apartment-level, variation was statistically significant. Meanwhile, the room type (i.e., living room or bedroom) did not significantly affect the relationship between outdoor environmental variables and the window open state. The strength of this study is that the modelling accounted for the hierarchical structure of the data by simultaneously considering room-and apartment- level behavioural variations. By quantifying the significant diversity of occupant behaviour in the natural ventilation of residences, future research can more accurately estimate the variation in building energy and indoor air quality impacts.

Model predictive control for Demand- and Market-Responsive building energy management by leveraging active latent heat storage

Active latent heat storage (ALHS) involving phase-change materials constitutes a promising energy-efficient solution for building energy management (BEM) by reshaping building energy demands without occupant comfort degradation. Current BEM systems based on conventional reactive control lack the level of control delicacy required to exploit the full potential of ALHS for BEM under certain conditions, such as highly dynamic electricity prices. This study proposes a smart model predictive control (MPC) approach for BEM to minimize the energy cost while maintaining the indoor climate by fully applying ALHS. More specifically, a reduced-order, high-fidelity state-space model (SSM) of ALHS is proposed for fast building control. An MPC framework considering highly dynamic electricity prices and ALHS dynamics is developed based on the proposed ALHS SSM integrated with a building SSM. A case study entailing a set of simulations is designed based on a single-family house with a space heating system, including an ALHS, ground source heat pump, and radiator. The proposed MPC approach, compared to conventional reactive control, enables substantial reductions in the electricity cost (ranging from 53.2% to 122.7% depending on the MPC settings and ALHS capacity), even financial gains under certain scenarios. Further analysis reveals that coupling ALHS with MPC is critical to ensure that ALHS adoption is economically convincing: while conventional reactive control of an ALHS-equipped building increases the electricity cost, an MPC-enabled building could reduce the electricity cost by 45.1% due to ALHS adoption. The proposed MPC approach also exhibits promising feasibility for real-world BEM applications.

Optimisation of the insulation thickness for the external walls in Morocco while considering the effect of climate change

The rise in global temperature is a direct consequence of the climate change phenomenon, whose effect on building energy demands can no longer be neglected. In this regard, thermal insulation is considered an efficient design measure to enhance the energy performance of buildings. The key factor for the cost-effective implementation of insulation in buildings is the proper selection of insulation thickness. However, the consideration of the climate change factor in the optimisation of the insulation thickness for building walls has not yet been addressed by researchers. In this work, the optimum insulation thickness of different wall façades is calculated in three principal climate regions in Morocco, while taking into consideration the effect of climate change over the building lifespan. The calculation used a dynamic life cycle costing method to consider the varying climate conditions over time. The future weather data is elaborated based on the medium–low greenhouse gas emissions scenario RCP4.5 of the intergovernmental panel on climate change fifth assessment report. The obtained results show that the optimum insulation layer thickness would vary between 0.04 m and 0.07 m depending on the wall orientation and the climate zone. Considering all walls, the life-cycle energy saving ratio would vary between 49.64 % and 54.87 %.The economic feasibility analysis indicates the payback period would be less than 10 years regardless of the wall orientation and the climate zone. In addition, it is shown that the effectiveness of the insulation would not be adversely affected by the climate change phenomenon in the future.

Thermal, mechanical and microstructural properties of sustainable concrete incorporating Phase change materials

Over the past two decades, there has been a significant increase in Buildings’ energy consumption. This rise led to a noticeable leap in operating costs. Subsequently, a need to find novel ways to lower the energy demand in buildings was crucial. Advances in material technology have proven that incorporating phase change materials (PCM) into concrete is one of the ways to tackle energy consumption problems. Depending on the temperature that PCM is exposed to, energy can be absorbed and released easily. This study introduces a set of experiments adopting different quantities of microencapsulated paraffin wax; 1%, 3%, 5%, 7%, and 10% incorporated into normal strength concrete (NSC), high strength concrete (HSC), normal lightweight concrete (LWC) and high strength lightweight concrete (HSLWC). Hence, fresh, hardened, and thermal properties of produced concrete were explored. The hardened properties comprise both strength and sorptivity tests. Additionally, microstructure analysis was investigated. Concrete thermal performance was identified by both thermal conductivity (K-Value) and analyzing the heat transfer rate. The results showed that increasing PCM amount led to reducing the thermal conductivity and increasing heat capacity, which in turn enhanced concrete thermal performance. However, the microstructural analysis showed that many capsules are damaged during the mixing process, releasing their paraffin wax filling into the surrounding matrix, which consequently causes a significant reduction in strength. Anyhow, the prevention of PCM leakage during mixing and improving ITZ in concrete were both achieved using HSC.

The efficiency of photovoltaic panels to meet energy demand of a school building in the Mesopotamia region

The efficiency of photovoltaic panels to meet energy demand of a school building in the Mesopotamia region

Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes.

In the energy system, various sources are used to fulfill the energy demand of large buildings. The energy management of large-scale buildings is very important. The proposed system comprises solar PVs, energy storage systems, and electric vehicles. Demand response (DR) schemes are considered in various studies, but the analysis of the impact of dynamic DR on operational cost has been ignored. So, in this paper, renewable energy resources and storages are integrated considering the demand response strategies such as real-time pricing (RTP), critical peak pricing (CPP), and time of use (ToU). The proposed system is mapped in a linear model and simulated in MATLAB using linear programming (LP). Different case studies are investigated considering the dynamic demand response schemes. Among different schemes, results based on real-time pricing (58% saving) show more saving as compared to the CPP and ToU. The obtained results reduced the operational cost and greenhouse gas (GHG) emissions, which shows the efficacy of the model.

Energy analysis of a phase change material embedded heat exchanger for air conditioning load reduction in different Indian climatic zones

Air conditioning (AC) systems for tropical countries like India account for sixty percent of the total energy needs of a building. With the onset of COVID-19, the increase of fresh air ventilation rate has been recommended by various guidelines for indoor spaces which increase the load on the AC system. The present study attempts to reduce this burden through retrofitting a phase change material (PCM) embedded pin fin heat exchanger into an air-conditioning system. The heat exchanger is designed to cater to the peak load fluctuations for cities in three hot climatic zones of India, viz., Jaisalmer, Kolkata, and Delhi. Dodecanol with a melting temperature of 24 °C, is chosen as the appropriate PCM material for these locations. The optimal pin fin diameters are estimated through an entropy generation minimization analysis for the three locations. A heat transfer analysis of the PCM embedded heat exchanger is further presented through an analytical approach to estimate the PCM mass requirement and energy savings potential. The masses of the PCM estimated for Jaisalmer, Kolkata, and Delhi are 11.36 kg, 22.42 kg, and 19.35 kg, respectively for their respective peak load fluctuations of 0.25 kW, 0.28 kW and 0.48 kW. Energy savings of up to 4.7 % for Delhi, 2 % for Kolkata, and 2.75 % for Jaisalmer are identified with the PCM embedded heat exchanger incorporation. The results show the potential of such PCM thermal storage in reducing the peak energy demands of buildings amidst various environmental and health concerns.

Parametric Performance Analysis of the Cooling Potential of Earth-to-Air Heat Exchangers in Hot and Humid Climates

Earth-to-air heat exchangers (EAHEs) are widely used to reduce the indoor temperature and associated cooling energy demand of buildings. This study investigated the potential reduction in indoor temperatures via energy-efficient ventilation through EAHEs in an existing architectural campus building (ACB) with an energy-efficient renovated building envelope in the hot and humid climate of Karachi, Pakistan. The building information modeling (BIM) program Autodesk Revit was used to develop a virtual ACB BIM model. An EnergyPlus parametric analysis of the ACB BIM model in DesignBuilder facilitated quantification of the influences of operating parameters such as pipe installation depth and pipe diameter for EAHEs with similar total pipe lengths and air-exchange rates on the performance of the EAHEs during the cooling season. A 3 m deep and 0.1 m diameter pipe layout in open space significantly reduces indoor temperature via a specific duct layout in an exemplary ACB. The results show that a pipe diameter above 0.1 m is unsuitable because of the reduction in convective heat transfer due to the increase in the pipe’s surface area and the decrease in pressure in the pipe. The findings of this study can be used to improve the indoor thermal comfort of buildings in climates with comparable properties.

Phase change materials for energy efficiency in building components – Overview

Energy demand in the building particularly in summer during peak hours due to outdoor climate and lack of sufficient high thermal mass construction materials. Advanced materials are required to reduce the recurring energy demand of buildings. This peak hour energy demand can be significantly reduced by using phase change materials (PCM). These PCMs may be incorporated into building components/ products in various ways. In this overview, the recent advances in the field of using different types of PCMs, parameters to be consider for building components, encapsulation of PCMs and influence of PCMs on thermal and mechanical properties of construction materials are briefly discussed. Importantly, behaviour of MPCM in building components is concisely elaborated in effective way.

A stochastic approach to feasibility analysis of boiler replacement in educational buildings in Extremadura (Spain)

BackgroundEnergy efficiency in buildings must be increased in order to reduce both energy intensity and greenhouse gas emissions. This study proposed the replacement of existing diesel boilers with biomass boilers, using four fuels (bulk pellets, wood chip, olive kernel and milled nutshell) to meet the energy demands of educational buildings in the region of Extremadura (Spain). High uncertainty surrounds biomass price prediction affecting the accuracy of economic feasibility analyses; thus, stochastic processes are suitable to support an improvement in the accuracy of predictions. The objective of the study is to demonstrate the feasibility of replacing diesel boilers with biomass boilers in order to revalorize agroforestry residues.ResultsA stochastic simulation of the feasibility of replacing oil-fired boilers with biomass-fired boilers was carried out in this research. Up to 20 million possible scenarios of 10 years of fuel price evolution were simulated by Monte Carlo method based on empirical price trends data. Regression models were built to relate Net Present Values with discount rates, whose statistical dependency was significant. Predictions on financial indicators showed biomass fuels as the most profitable investment, rather than fuel oil. Specifically, in this study, milled nutshell was found the most profitable fuel in the simulation runs, with Net Present Value = 27,151.09 € (standard deviation = 7939.88 €) and Internal Rate of Return = 16.9% (standard deviation = 3.4%).ConclusionsContinuing to use oil-fired boilers costs more than the purchase and operation of new biomass-fired boilers, since the latter produce a higher cumulative cash flow than the initial investment within the next years. The payback period lies within the range of 4 to 6 years depending on the type of biomass fuel. Getting on the path to sustainability in education buildings can reduce up to 94.4% GHG emissions. This research contributes to promoting the use of low-emission fuels to meet the energy demand of educational buildings. Its results will have a positive effect in the region of Extremadura (Spain), as it boosts the appreciation of agro-industrial waste and economically strengthens the sector.