Building Energy Demand Research Articles

The effects of climate change on the energy demand of residential buildings: A case study of a coastal city

The effects of climate change on the energy demand of residential buildings: A case study of a coastal city

Exploring energy consumption for less energy-hungry building in UK using advanced aerogel window

A common strategy for employing net zero energy buildings (NZEBs) in the United Kingdom (UK) to achieve the national target for 2050 is improving the thermal insulation in glazing units to reduce the space heating load. This study examines the impact of aerogel technologies as an effective strategy for window retrofits under the UK climate. Eleven samples of granular and monolithic aerogel windows were selected across various literature to illustrate the optical properties and thermal characteristics of each glazing structure. In Falmouth, UK, a two-story residential building was selected for simulation to assess the thermal efficiency of various aerogel systems as alternatives for standard double-glazing systems. Results revealed that aerogel technology has high potential in increasing the total solar heat energy through windows by occupying high SHGCs up to 0.738 and low U-values down to 0.381 W/m2K. Monolithic aerogel has more energy-saving potential and can allow greater daylight penetration compared to granular aerogel with a visual transmittance of up to 69%. Increasing the WWR can help reduce the heating load of the building but can also cause the cooling loads to increase from internal overheating. The south-facing facade of the building where most of the windows were present experienced higher cooling loads, indicating that the addition of aerogel glazing causes overheating during summer periods. Applying aerogel for window insulation could increase the total window heat transfer by up to 33% and reduce heat demand by 15.5% compared to a standard double-glazing system. As a result, aerogel technology can prove to be a viable option for decreasing the building energy demand for space heating in the UK.

Adaptable pathway to net zero carbon: A case study for Techno-Economic & Environmental assessment of Rooftop Solar PV System

Several countries have a critical need to enhance their electricity production to satisfy the tremendously increasing energy demand and to minimize the black outs. Due to scarcity of fossil fuels and degrading environment, rooftop Solar Photovoltaic (SPV) can be a suitable renewable alternative of green and clean energy. In this paper, due to the techno-economic, environmental and social benefits of Solar PV, the modelling and planning of a solar PV plant to fulfil the energy demand of an academic building is carried out. The analysis is done including all the financial and environmental aspects considering recent market prices and future price escalation. A Modified Greedy Search based optimal planning of the SPV to minimize grid dependence considering load variation pattern with its assessment analysis for an academic building is proposed in this research work. The various financial parameters that affect the feasibility of Solar PV plant i.e., energy payback period, grid dependency, rate of investment return, economic payback period, rate of escalation with actual cost of electricity, annual maintenance cost, depreciation and taxation are considered and evaluated. The environmental factors related to the generation from Solar PV i.e., CO2 emission reduction and carbon credit are also included in analysis. The results obtained for the proposed Solar PV plant shows that the energy payback period is 8 years and IRR with depreciation and taxation is 21% (approx.) with net CO2 mitigation of 1199 tCO2 during the useful life span (25 years) of the plant.

Energy consumption predictions by genetic programming methods for PCM integrated building in the tropical savanna climate zone

The development of energy-efficient buildings by considering early-stage design parameters can help reduce buildings' energy consumption. Machine learning tools are getting popular for forecasting the energy demand of buildings, which play a vital role in improving building energy efficiency. In this research, multi-expression and genetic expression programming were utilized to anticipate the energy consumption of PCM-integrated buildings by taking early-stage design parameters into consideration. The prediction models were developed using the data generated by energy simulations for the PCM-integrated building in eight cities within a tropical savanna climate. The statical parameters were used to evaluate and externally validate the proposed prediction model. The statistical evaluation reveals that the genetic expression programming-based predictive model gave more accurate energy consumption predictions for PCM-integrated buildings than multi-expression programming. The performance indices of the statistically analyzed gene expression programming-based prediction model (GEP7) showed excellent values: correlation coefficient (R) = 0.961, performance index (ρ) = 0.169, and Nash-Sutcliffe efficiency (NSE) = 0.108. Thereafter, the sensitivity and parametric analyses were performed. It was unearthed that the roof solar absorptance, window visible transmittance, wall solar absorptance, and the melting temperature of PCM were the influential early-stage design parameters for PCM-integrated buildings. In conclusion, the gene-expression programming-based predictive model can be utilized to predict the influence of early-stage design parameters on the energy consumption of PCM-integrated buildings.

Biomass supply chain resilience: integrating demand and availability predictions into routing decisions using machine learning

ABSTRACT Biomass sources have the potential to mitigate carbon emissions as a renewable source while reducing waste and residues. Seasonality and disruption risks are some of the disadvantages of biomass resources requiring that biomass supply chains be managed such that to withstand disruptions. There has been very limited research on integrating predictions for smart management on supply or demand sides of biomass supply chains. In this study, a number of predictive models are investigated for building energy demand and biomass stock availability subject to forecasts of weather conditions. On that basis, an allocation algorithm is proposed for optimal collection and logistics of biomass from land to depots. Accordingly, Google Maps API will be used to identify the best distribution routes for delivering biomass from depots to end-users. A case study with real (supply and demand) data is considered. The proposed integrated data-driven approach aims at improving the accuracy of biomass supply and demand predictions and coordinating these predictions to enhance the resiliency of bioenergy supply chain routing decisions.

PCM-based ceiling panels for passive cooling in buildings: A CFD modelling

To relieve the upsurge of the energy demand associated to building indoor cooling, passive cooling employing phase change materials (PCMs) has been proposed as a promising technique to stabilise indoor air temperature fluctuation and reduce building energy demand. PCMs can exchange a large amount of latent heat during the melting and solidification processes, which can be utilised in line with the discharging and charging processes of a cooling system. The use of encapsulated PCM ceiling panels in buildings has immense application potential for passive cooling, which has attracted investigations in recent years. The geometry selection of the PCM ceiling panel is vital as it determines the exposed surface area to the surrounding air and the natural convection, and therefore the heat transfer rate to/from the PCM, which generally suffers from low thermal conductivity. This paper builds on the geometric optimisation of enclosed PCM capsules driven by natural convection, aiming to investigate the phase change behaviour and thermal performance of encapsulated PCM ceiling panels of different geometries. Here, the presented study focuses on the conjugate heat transfer phenomena through the surrounding air, panel shell, and PCM. A mixture of lauric, capric, and oleic acids was selected as PCM encapsulated in a thin-shell enclosure made of acrylonitrile butadiene styrene plastic, which forms the aesthetic panels attached to the ceiling. A computational fluid dynamics model was validated using full-scale experimental data and further employed to analyse the discharging process and design considerations of the PCM ceiling panels. The effects of various influencing factors were explored, such as panel volume, the initial temperature difference between PCMs and surrounding air, shell thickness, and geometrical features. The results indicate that the panel comprised of a pyramid array is more advantageous than the tetrahedral-based panel in terms of thermal performance, where the average melting rate is 20.8 % higher when the panel volume is 250 mL. In addition, tweaking the geometric features of the panel will largely impact its thermal performance as the effective heat transfer area and natural convection conditions are varied. Finally, it was found that hanging the panel to the ceiling will effectively enhance the heat transfer compared to directly attaching it to the ceiling. The results complement the knowledge body of macro-encapsulated PCM ceiling panels in terms of thermal performance and geometric optimisation. The effects of geometric features on the thermal performance of the PCM ceiling panels are studied in a more holistic manner.

Retrofitting for Improving Indoor Air Quality and Energy Efficiency in the Hospital Building

A growing body of research shows that retrofitting practices can potentially reduce energy demand in hospital buildings and enhance indoor air quality. Yet, there is a lack of comprehensive reviews associated with green retrofitting practices in hospital buildings. This study aimed to undertake a systematic review of the past literature on retrofitting techniques used to improve indoor air quality and energy efficiency in hospital buildings, to identify barriers to its uptake, and to recommend solutions to identified challenges. For this reason, a systematic review was conducted for the published research from various academic databases. Findings showed a growing uptake of various retrofitting strategies for indoor air quality in hospital buildings. As a result, hospital management or building owners might consider addressing these challenges to facilitate the uptake and implementation of retrofitting practices in their facilities.

Simulation of thermal comfort and energy demand in buildings of sub-Himalayan eastern India - Impact of climate change at mid (2050) and distant (2080) future

The global warming associated with climate change predicted by the Intergovernmental Panel on Climate Change (IPCC) is expected to deteriorate the indoor climate of free-running buildings. Features like proper orientation and wall thickness are important for the design of a building that is resilient to the impact of climate change. However, the implementation of these features is sometimes difficult, especially in a rugged hilly location. A whole building simulation was performed using DesignBuilder for an existing 3-storey free running multi-family concrete building located in the sub-Himalayan region of eastern India, for thermal comfort and energy demand during the present and the climate change scenarios of 2050 and 2080. The results show an increasing trend in the indoor operative temperature during the future climatic scenario, with the condition inside the top roof-exposed floor deteriorating the most. A decrease of 59.8% and 81.2% in the annual heating energy and an increase of 221.9% and 467.0% in the annual cooling energy were predicted for the future climate of 2050 and 2080 compared to the present. Parametric analysis performed considering orientation, wall U-value, infiltration rate and window-to-wall ratios revealed that the east/south-east facing orientation would perform the best with regard to overheating due to climate change. Further, the use of autoclaved aerated concrete (AAC) brick is recommended along with the decrease in air infiltration rate and window-to-wall ratio to improve the thermal performance of the indoor environment. In addition, we have also proposed a method to assess the under-cooling of an indoor environment.

Energy futures of representative Swiss communities under the influence of urban development, building retrofit, and climate change

Reducing energy demand in buildings is an integral part of many climate change mitigation strategies. Yet, the prospected development of communities is often overlooked when estimating future energy demand. Here, we investigate the future energy demand in representative Swiss communities, considering climate change projections, building retrofit and urban development. Following a scenario-based approach we model urban, suburban and rural community archetypes under changing boundary conditions and different time scales using the City Energy Analyst an open-source computational framework. The results demonstrate that the future energy demand of Swiss communities is highly dependant on their development trajectories regarding population growth, occupant density and building use-types. For the urban archetype, the most significant result is the increase of annual space cooling which by 2060 could be comparable to space heating. For the sub-urban, increases in energy demand due to urban development were observed despite retrofit measures, whereas the rural archetype displays high space heating demand across all scenarios. Consequently, predictions for future energy demand at the community scale without considering urban development trajectories are likely to be incomplete. The results demonstrate the relevance of increasing the modelling scale from national to community scale to support decision making on different levels of governance.

Applying machine learning based on multilayer perceptron on building energy demand in presence of phase change material to drop cooling load

In this study, by developing a multi-layer neural network, energy consumption in buildings with phase change material (PCM) was investigated. Three input parameters including transition temperature, setpoint, and PCM thickness were combined in a way that covers 324 different cases. The results of the neural network indicated that it is possible to predict the amount of cooling load with an accuracy of more than 95%. In a situation where the temperature is set at 20°C, if PCM with a melting point of 26°C is added with a thickness of 10 cm, the cooling demand experience a sharp decrease by 373.8 kWh/m2. Considering energy saving of 59.1 kWh/m2 for heating load, the annual energy demand of the building declined by 432.9 kWh/m2. As the setpoint increases, the effect of the phase change becomes more intense in heating demand than in cooling one. At a setpoint of 28°C, only the PCM with a phase change temperature of 29°C is useful and in this case, the cooling/heating loads are reduced by 242.7 and 218 kWh/m2 which resulted in annual energy saving of 460.7 kWh/m2. The neural network showed that if the internal temperature is adjusted between 22 and 28°C, the phase transition of 29°C is the most suitable case.

Mapping Seasonal Variability of Buildings Electricity Demand profiles in Mediterranean Small Islands

In communities with a high tourist influx and warm climate, such as Mediterranean small islands, the energy demands for space cooling and domestic hot water are expected to significantly increase during summer. Considering the current energy context, for succeeding energy transition and reducing the dependency on not endogenous fossil fuels, it is paramount to increase the deployment of renewable sources of energy, especially wind and solar which, however, are aleatory and unpredictable. Hence, to reduce the high costs for energy supply in these contexts, the analysis of the variation of energy consumption is fundamental. Moreover, mapping the spatial distribution of energy profiles can be useful to have an overview at a large scale of the considered building stock. Within this frame, a Geographic-Information-System-based procedure was implemented to estimate the residential buildings energy demand profiles, focusing on the seasonal variation. The adopted method can provide a valid supporting tool for decision makers that have to implement smart energy strategies in contexts with a high variation of the energy demand and evident electricity summer peaks. The method for mapping the energy demand profiles, implemented on the small island of Pantelleria, can be applied to other similar contexts, also supporting energy policies in the implementation of renewable energy communities.

Near-optimal adaptive predictive control model study for roller shades in office spaces

As improved building energy efficiency becomes mandatory, dynamic shading systems are expected to reduce building energy demand because their control mechanisms are increasingly self-adaptable to changes in the environment. Various shading control strategies had been proposed and compared in existing literature, but their energy performance can be further improved with a more comprehensive consideration of environmental changes and indoor demand. This study develops and validates an adaptive predictive control (APC) model for roller shades that combines simulation-based optimization of shading control with machine learning. The model predicts the appropriate position of roller shades based on multiple environmental parameters, minimizing the building energy demand. EnergyPlus and Python provide continuous energy simulation and data processing. Using pre-simulated datasets, machine learning algorithms develop APC models. The study scenario is a typical office space in Guangzhou, China. Quantitative analysis of the simulation results elucidates the effectiveness of the method. The performance of the model was evaluated using historical meteorological data from 2020. The proposed control model was able to achieve nearly optimal energy efficiency, reducing energy demand by 38.3% while reaching annual Useful Daylight Illuminances of 72.9%.

Implementation of a Long Short-Term Memory Transfer Learning (LSTM-TL)-Based Data-Driven Model for Building Energy Demand Forecasting

Building energy consumption accounts for about 40% of global primary energy use and 30% of worldwide greenhouse gas (GHG) emissions. Among the energy-related factors present in buildings, heating, cooling, and air-conditioning (HVAC) systems are considered major contributors to whole-building energy use. To improve the energy efficiency of HVAC systems and mitigate whole-building energy consumption, accurately predicting the building energy consumption can play a significant role. Although many prediction approaches are available for building energy use, a machine learning-based modeling approach (i.e., black box models) has recently been considered to be one of the most promising building energy modeling techniques due to its simplicity and flexibility compared to physics-based modeling techniques (i.e., white box models). This study presents a building energy load forecasting method based on long-term short-term memory (LSTM) and transfer learning (TL) strategies. To implement this approach, this study first conducted raw data pre-processing analysis to generate input datasets. A hospital building type was considered for a case study in the first stage. The hospital prototype building model, developed by the U.S. department of energy (DOE), was used to generate an initial input training and testing dataset for source domain tasks before the transfer learning process. For the transfer learning process in a target domain, a simulation-based analysis was also conducted to obtain target datasets by assuming limited data lengths in different weather conditions. The training and testing procedures were performed using separate cooling and heating periods with and without the transfer learning process for source and target domain tasks, respectively. Lastly, a comparative analysis was carried out to investigate how the accuracy of LSTM prediction can be enhanced with the help of transfer learning strategies. The results from this study show that the developed LSTM-TL model can achieve better performance than the prediction model, which only uses LSTM under different weather conditions. In addition, accurate performance can vary according to different transfer learning methods with frozen and fine-tuning layers and locations.

In situ U-value measurement of building envelopes through continuous low-cost monitoring

Accurate characterization of the transmittance parameter (U-value) is decisive when assessing energy demand or energy consumption of buildings. The U-value of building envelopes is usually estimated using commercial devices. These commercial solutions present several drawbacks such as their high cost, the fact that they require contact sensors to measure the surface temperature on a limited number of points and the difficulty to meet the standards for ideal measuring conditions. To solve these issues, a Hyper Efficient Arduino Transmittance-meter (HEAT) was recently proposed by the authors for monitoring the U-value based on low-cost technology. This system is now completed with the recording of time series of data based on Internet of Things protocols and the use of non-contact temperature sensors. Studying the applicability, in this case HEAT is used for characterizing walls and ceiling of a building room. To check its performance, the obtained results were compared with those derived from standards and conventional building modelling tools. The results show that the developed system is reliable according to the U-values obtained and expected for the monitored elements, with an acceptable range of uncertainty compared to the ones presented in the literature.

A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system

Forecasting the short-term future energy demand in buildings and districts is a vital component towards the optimization of energy use and consequently the reduction in greenhouse gas emissions. This paper explores artificial intelligence approaches applied to estimate the future heating load in a district heating system. A distinction is made within thisd work between a prediction and forecasting based approach; a comparison is then accomplished by applying each method with prominent Machine Learning and Deep Learning based algorithms to estimate the future heating demand over 6 h and 24 h ahead. This analysis used available data from a Canadian district heating system in Quebec and actual weather forecasts obtained from Canadian meteorological services. All models within this work applied a grid search in order to calibrate their respective hyperparameters. Results of this work indicated that the prediction-based approach (with forecasted inputs) obtained a higher accuracy than the forecasting approach. All the machine learning models obtained good accuracy with errors not exceeding 16% CV(RMSE) and closer to 10% CV(RMSE) for the top performing models. Furthermore, the LSTM and XGBoost were consistently among the top performing algorithms and provided good performance over a variety of hyperparameters. The biggest difference between the two algorithms was the computational times; it was observed that the XGBoost was significantly faster to train.

Review on the Life Cycle Assessment of Thermal Energy Storage Used in Building Applications

To reduce building sector CO2 emissions, integrating renewable energy and thermal energy storage (TES) into building design is crucial. TES provides a way of storing thermal energy during high renewable energy production for use later during peak energy demand in buildings. The type of thermal energy stored in TES can be divided into three categories: sensible, latent, and sorption/chemical. Unlike sensible TES, latent TES and sorption/chemical TES have not been widely applied; however, they have the advantage of a higher energy density, making them effective for building applications. Most TES research focuses on technical design and rarely addresses its environmental, social, and cost impact. Life cycle assessment (LCA) is an internationally standardized method for evaluating the environmental impacts of any process. Life cycle sustainability assessment (LCSA) is an expansion of LCA, including economic and social sustainability assessments. This paper aims to provide a literature review of the LCA and LCSA of TES, specifically for building applications. Concerning the low technology readiness level (TRL) of several TES systems, the challenges and benefits of conducting LCA for these systems are highlighted. Furthermore, based on published studies on emerging technologies for LCA, a suggested procedure to carry out the LCA of TES with low TRL is presented.

Modeling energy-efficient building loads using machine-learning algorithms for the design phase

Very little work has been done on the feasibility of Machine Learning (ML) for predicting buildings energy demand right at the design stage. This feasibility, if proven, would help to avoid the construction of inefficient buildings. This paper uses dataset from 7559 buildings, and estimates their energy consumption using nine ML models. Results show that deep neural network (DNN) is the most efficient ML model with MAE, MSD and RMSE of 0.93, 1.12 and 1.06 respectively achieved in less than 7 s despite the huge data size. Its r2 is also the highest (0.96) which means that the DNN approach manages to explain 96% of the energy consumption in buildings and only 4% remains unexplained certainly due to the limitation of independent variables. Also, this result is not affected by building clusters nor by data from a particular climate zone. As an innovation, this study proposes a model that professionals could use in the design phase of a construction project. This model will allow them to take into account all crucial aspects of the design of an energy efficient building. The model will then serve as a decision-making tool to control and optimise the project and to anticipate energy consumption even before the building is constructed.

Climate change shifts the trade-off between lower cooling and higher heating demand from daylight saving time in office buildings

The original intention of daylight saving time (DST) was to save energy required for artificial lighting. This one-hour shift in working hours, however, also impacts the current and future heating and cooling demand of buildings, which is yet to be thoroughly investigated. Here, daylight saving time-induced heating and cooling demand of archetype offices across the United States are simulated for 15 cities for different representative concentration pathway (RCP) climate trajectories. DST reduces cooling more than it increases heating. Maximum savings of up to 5.9% for cooling and 4.4% increase in heating were simulated under current climatic conditions, noting that cooling dominates the buildings’ demand during the DST period. Climate change increases future cooling demand, but does not significantly affect the combined (heating and cooling) potential of reducing energy demand when DST is introduced. However, the relative reduction (i.e. decrease in the percentage of total cooling demand) is smaller when considering climate change. The impact of DST on cooling and heating energy demand depends on the geographical location, which determines the amount and temporal pattern of cooling and heating demand. For the considered case studies, introducing DST with climate change generally resulted in overall combined savings with a maximum saving of 3% for Port Angeles, assuming an RCP 4.5 scenario. Policies that shift working hours need to be evaluated considering their impact on building energy demand and it is necessary to establish whether saving cooling or saving heating energy demand can achieve higher CO2 emission reductions.

An agile heating and cooling energy demand model for residential buildings. Case study in a mediterranean city residential sector

Climate change will affect people's health, especially in cities. Hence, energy planning will play a key role in the development of sustainable and resilient cities. Urban building energy models facilitate energy planning as heating and cooling demand becomes known, and the consequences of different planning actions can be modelled. This research presents an agile heating and cooling demand model. It combines a European standard's methodology, geometric information of buildings collected from cadastral and altimetric datasets using GIS-based technologies, solar irradiation analysis and the degree-days method. The model is validated with various case studies and then applied to several buildings in different environments.The model shows the strong influence of the building's age (design and materials), the building surface-to-volume ratio on the energy demand and the importance of the solar irradiation analysis. Furthermore, the model can predict the effects of the temperature rise on energy demand and prioritise the buildings to be retrofitted. Indeed, one of the conclusions obtained from the model is that advanced retrofitting of 17% of the most energy demanding buildings would obtain a 50% decrease in thermal demand; if the percentage was 50%, an 85% reduction could be reached. In conclusion, the energy planning tool hereby presented is a useful tool to viably foresee the energy demand of residential buildings and districts and the effects of climate change on their energy demand, as well as the consequences of countermeasures like retrofitting.

AutoBPS: A tool for urban building energy modeling to support energy efficiency improvement at city-scale

China has promised to peak carbon emissions by 2030 and become carbon neutral before 2060. Achieving this target requires actions from all sectors, especially the building sector, which accounts for 33 % of the total energy consumption in China. Within this scenario, this paper introduces a new urban building energy modeling tool, Automated Building Performance Simulation (AutoBPS), to calculate urban building energy demands and to analyze energy retrofit and rooftop photovoltaic (PV) potential. AutoBPS uses a building-by-building approach to automatically generate multi-zone (one zone per floor) models based on the city building dataset, considering the shading effect by surrounding buildings. The tool simulates building energy use and PV generation via EnergyPlus. A downtown district of 3633 buildings in Changsha, China, was selected as a case study to demonstrate the features of the tool. Firstly, annual baseline energy uses of 1793 GWh were simulated by inputting Geographic Information System (GIS) data. Then, different energy conservation measures (covering envelope, lighting, cooling system upgrade, etc.) and roof PV generation systems were adopted to explore energy saving potential. The results showed that the window upgrade was the most energy-efficient individual measure for residential buildings, while the lighting upgrade was that one for commercial buildings. Altogether three measures could save 2.7 ∼ 30.7 % of annual site energy use intensity per building. The total energy demand could be reduced by 18.5 % by combining all measures and saved by 38.6 % when PV installation was added. AutoBPS can be easily applied to other cities, supporting urban energy efficiency plans for carbon reduction goal implementation.