Reducing Building Energy Demand Research Articles

A phase change material (PCM) based novel retrofitting approach in the air conditioning system to reduce building energy demand

Improvement in air quality and reduction of airborne diseases can be effectively achieved by inducing fresh air into the building. However, this challenges the building’s energy consumption. As a proposed solution, the present study dwells into the use of phase change materials (PCM), a heat-based exchanger, as a retrofit with a Heating Ventilation and Air-conditioning system (HVAC), to extract thermal energy from fresh air and consequentially diminish the energy demand. Calcium chloride hexahydrate (CaCl2·6H2O) is used as PCM, selected by TOPSIS method based on entropy weights. The energy saved in the augmented PCM thickness (from 20 mm to 100 mm) and added longitudinal fins (5 mm in width and 50 mm in length) is assessed in comparison to the standard HVAC systems utilizing R134a. For summers in composite climate, the HVAC system retrofitted with a heat exchanger with 100 mm PCM thickness 48 fin configuration achieved peak and average energy savings of 12 % and 9 %, respectively, when the system operated for 6 h. For PCM thicknesses of 75 mm, 50 mm, and 20 mm, the maximum energy savings were 6.64 % with 48 fins, 5.22 % with 24 fins, and 3.22 % with 12 fins, respectively. The study provides policymakers with energy-efficient and sustainable solutions for HVAC systems, which can reduce energy demand and help combat climate change.

Supporting climate-neutral cities with urban energy modeling: a review of building retrofit scenarios, focused on decision-making, energy and environmental performance, and cost

Climate deadlines are fast approaching, and city action plans must address slashing carbon emissions using scarce financial resources. Urban energy modeling (UEM) supports building sector transformation by quantifying energy use and emissions in baseline and retrofit scenarios. Additionally, many UEM studies evaluate retrofit costs and financial returns. This review of 26 UEM studies critically analyzes how studies decide to model retrofit measures or impose scenario constraints, and how energy, emissions, and costs are quantified. The results show divergent quantification approaches among the reviewed literature, hindering the usefulness and comparability of the studies for policymakers. The findings also indicate challenges with renewable energy production and heating via heat pumps, including increased peak electrical loads and seasonal mismatches in generation and consumption, which are mitigated by measures reducing building energy demand and energy use, demand-response measures to curtail peaks, and district sources of endogenous energy. The value of the decision-making analysis is to signal pathways toward innovation and stakeholder collaboration for city plans, highlighting approaches using context analysis to generate and share energy in districts, economic criteria to mirror real-world conditions, and stakeholder engagement to meet local priorities. Finally, perspectives for future UEM studies support policymakers guiding the transformation to climate neutrality.

Green roof cooling and carbon mitigation benefits in a subtropical city

Green roofs are promoted as an effective nature-based urban heat island mitigation strategy. Green roof cooling and energy-saving benefits have been simulated for various climatic zones, but mainly at the building scale. Due to a lack of fact-based information on neighborhood cooling benefits, green roof construction lags and has rarely been incorporated into urban planning actions. This study investigated the thermal benefits and energy savings of green roofs for the central area of the Xianlin Campus of Nanjing University at the neighborhood scale. Three scenarios were simulated for a hot summer day using a validated ENVI-met model: a base case (S0), extensive green roofs (EGRs) (S1), and intensive green roofs (IGRs) (S2). The air temperature cooling benefit from green roofs extended downwards to the pedestrian level. The EGR scenario achieved a maximum 0.29 °C air temperature reduction at the pedestrian level and 0.37 °C at the rooftop level. The IGR scenario achieved a maximum 0.35 °C air temperature reduction at the pedestrian level and 0.45 °C at the rooftop level. EGRs and IGRs reduced energy demands for air-conditioning by 0.39 kWh·m−2·d−1 and 0.56 kWh·m−2·d−1 and CO2 emissions by 31,997 kg·d−1 and 45,967 kg·d−1, respectively. These results confirm that green roofs yield substantial cooling and carbon mitigation benefits. Our study provides essential data to establish green roofs as mainstream cooling technology in subtropical cities. The results also imply that urban planners and policymakers may need to embrace the implementation of green roofs in long-term planning and building design practices to improve urban thermal environments, reduce building energy demand, and curb carbon emissions.

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.

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.

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%.

Net energy and cost benefit of phthalocyanine and heptamethine transparent photovoltaics in commercial buildings

There is a growing interest in using transparent organic photovoltaics (TOPVs) to generate on-site electricity and reduce building energy demand. This study evaluates the use of chloroaluminium phthalocyanine and heptamethine-based TOPVs in five types of commercial buildings and four climates in the U.S. The building energy demand is modeled with and without TOPVs, along with the electricity generation from the TOPVs, and the cradle to gate life-cycle cumulative energy demand to manufacture the TOPV modules on glass. The net energy benefit is positive for all five commercial buildings in all four locations meaning that more energy is produced or saved than used in manufacturing TOPVs. The energy payback times for TOPVs are less than other building integrated PV technologies. All commercial buildings save energy, cost, and greenhouse gas emissions with TOPVs, and the best applications depend on the building’s window-wall ratio, occupancy, and usage. In the future, the spectral properties of TOPVs could be tailored to save more cooling and heating energy based on the building’s structure, climate, and energy demand.

Energy saving potential of photovoltaic windows: Impact of shading, geography and climate

Engineering the thermal and optical properties of windows is a key to reducing building energy demand, which constitutes a major portion of worldwide electricity consumption. Depositing thin-film photovoltaic coating that simultaneously generates power and allows visible light can be a viable route towards nearly zero energy building (nZEB). Such windows with Semi-Transparent Photovoltaics (STPV) have unique physical properties that require comprehensive modeling to properly estimate the performance. In this paper, we adopt an integrated approach to unify device simulation – yielding electrical, optical and thermal parameters – with building energy simulation using EnergyPlus and a Matlab based power generation platform. The power generation code contains models for shadow from nearby buildings, geography and climate dependent solar insolation and ambient temperature variation to predict efficiency accurately. We also develop a thermal model to find the U-value of a glass coated with STPV. We find that such coating dramatically improves insulation by reflecting infra-red radiation. Our results show that – contrary to roof-top solar panels – STPV modules, when put on all four vertical surfaces, produces significant output in the presence of both shadow and cloud. The net energy footprint of a building with a perovskite based STPV is found to be in the range of 45–80 KWh/m2-yr, well below the source energy use intensity goals set by different countries.

A New Hybrid MCDM Model for Insulation Material Evaluation for Healthier Environment

One of the easiest and most common methods for effectively reducing building energy demand is the selection of adequate thermal insulation materials. Thermal insulation is a substantial contribution and an evident, logical and practical first stage toward improving energy performance, particularly in envelope-load-dominant structures located in difficult climate zones. Today’s insulating materials come in a broad variety of sizes and shapes, each with its a own qualities. It is well acknowledged that material selection is one of the most difficult and time-consuming aspects of a construction project. Therefore, choosing the right insulation material is also a very important topic to increase energy efficiency. However, it is a complex problem with many criteria and alternatives. This study integrates three different multi criteria decision making methods, which are Fuzzy Best-Worst Method, CRiteria Importance Through Inter-criteria Correlation and Mixed Aggregation by COmprehensive Normalization Technique. In this study, the following eight criteria were taken into account in the evaluation: thermal conductivity, periodic thermal transmittance, specific heat, density, decrement factor, surface mass, thermal transmittance, and thermal wave shift. The first method will be used to find the subjective weights, while the second method will be used to find the objective weights. The third method will be used to rank the insulation materials. According to the results of the Fuzzy Best-Worst Method, the most important criterion was determined as thermal conductivity. According to the results of the CRiteria Importance Through Inter-criteria Correlation, the most important criterion was determined as thermal wave shift. According to the results of the Mixed Aggregation by COmprehensive Normalization Technique, the top 10 insulation materials are as follows: polyisocyanurate, polyurethane (1), polyurethane (2), wood fiber (1), kenaf, jute, cellulose (2), wood fiber (1), XPS (1) and XPS (2). According to the results of the proposed method, polyisocyanurate was determined as the best insulation material for healthier environment. This study makes two contributions to the literature: first, a new hybrid method was developed in this study. Secondly, in this study, the newly introduced Mixed Aggregation by COmprehensive Normalization Technique method was used.

Integrative economic framework incorporating the Emission Trading Scheme (ETS) for U.S. Residential energy systems

• The use of renewable sources, such as geothermal (GSHP) and solar energy (PV-integrated systems), turned out to be highly beneficial in reducing building energy demand, operation cost, and GHG emissions. • However, these renewable systems do not guarantee short payback periods. The payback period of ground-source heat pump (GSHP) is relatively long (8.08 to 19.75 years) compared to its high energy efficiency. • For most of the HVAC systems covered in this study, the payback period reduction is between 1.50 and 2.00 years when applying the current ETS policy (California cap and trade). • ‘Emission Trading Scheme (ETS) can shorten the payback periods for all HVAC systems covered in this study. Meanwhile, ‘solar tax credit’ can only reduce the payback periods for PV-integrated systems. • The growing demand for ‘Emission Trading Scheme (ETS)’ will reduce the payback periods for many residential HVAC systems, especially by encouraging the greater use of renewable energy sources. Throughout the past few decades, the residential building sector has been responsible for an increasingly large share of primary energy demand (21.2%) in the United States (EIA, 2020). Such high building energy demand from the residential sector will eventually cause both economic and environmental problems. Therefore, this study provides an integrative framework for evaluating the economic value of multiple HVAC systems in the U.S. residential sector. Specifically, this study calculates the payback period for a number of building energy systems by applying two environmental policies, the ‘solar tax credit’ and the ‘Emission Trading Scheme (ETS)’. A two-story single residential building in Detroit, MI was examined using the TRNSYS software tool. The building systems were subdivided into passive (insulation level) and active (HVAC type) systems. The insulation levels were classified into three scenarios depending on their respective U-values (low, medium, and high). Similarly, the HVAC systems were classified into twelve different types: (1) furnace and window cooling unit (benchmark), (2) air-source heat pump and air-conditioner (ASHP + AC), (3) ground-source heat pump (GSHP), (4) variable refrigerant flow (VRF), (5) VRF integrated with PV (PV + VRF), and (6) VRF integrated with PV and battery (PV + ESS + VRF). Each system can be operated with or without a heat recovery system. The results clearly show that the ground-source heat pump (GSHP) and PV-integrated systems (PV + VRF) are far more efficient than other conventional systems in reducing building energy demand, operation cost and GHG emissions. However, in contrast to these findings, the study also shows that both renewable energy systems have relatively long payback periods (8.08 to 20.50 years) along with their high system efficiencies. This clearly demonstrates that evaluating the overall efficiencies of building energy systems without performing additional economic analysis can mislead the public about the notion of ‘building energy optimization’. This could lead to suboptimal decision-making, whereas using the proposed framework would enable economically and environmentally preferable choices. In addition, this study presents support for the widespread application of ETS to the U.S. residential buildings. The growing demand for ETS will reduce the payback periods for many residential HVAC systems, especially by encouraging the greater use of renewable energy sources. In conclusion, since the economic incentive provided by ETS rewards taking an integrative approach to reduce building energy demand, operation cost and GHG emissions, adopting this policy actively at the national level will guarantee not only economic benefits for individual building owners, but also positive environmental impacts for the entire society.

Performance Evaluation of Thermal Bridge Reduction Method for Balcony in Apartment Buildings

Most apartment buildings in South Korea use internal insulation systems to reduce building energy demand. However, thermal bridges such as balcony slabs in apartment buildings still lead to significant heat loss in winter, because the internal insulation system is not continuous in the balcony slab structure, and floor heating systems are commonly used in residential buildings. Therefore, this study investigates two types of thermal break elements, namely thermal break (TB) and thermal break-fiber glass reinforced polymer (TB-GFRP), to improve the thermal resistance of a balcony thermal bridge. To understand the effects of balcony thermal bridges with and without thermal break elements, the linear thermal transmittances of different balcony thermal bridges were analyzed using Physibel simulations. Then, the heating demand of a model apartment under varying thermal bridge conditions was evaluated using TRNSYS simulations. To understand the effect of insulation systems on heat loss through a balcony thermal bridge, apartments with internal and external insulation systems were studied. Whether the apartment was heating was also considered in the thermal transmittance analysis. Thus, the linear thermal transmittance of the thermal bridges with thermal break elements was reduced by more than 60%, and the heating energy demands were reduced by more than 8%.

Control Strategies for Daylight and Artificial Lighting in Office Buildings—A Bibliometrically Assisted Review

A significant proportion of the total energy consumption in office buildings is attributable to lighting. Enhancements in energy efficiency are currently achieved through strategies to reduce artificial lighting by intelligent daylight utilization. Control strategies in the field of daylighting and artificial lighting are mostly rule-based and focus either on comfort aspects or energy objectives. This paper aims to provide an overview of published scientific literature on enhanced control strategies, in which new control approaches are critically analysed regarding the fulfilment of energy efficiency targets and comfort criteria simultaneously. For this purpose, subject-specific review articles from the period between 2015 and 2020 and their research sources from as far back as 1978 are analysed. Results show clearly that building controls increasingly need to address multiple trades to achieve a maximum improvement in user comfort and energy efficiency. User acceptance can be highlighted as a decisive factor in achieving targeted system efficiencies, which are highly determined by the ability of active user interaction in the automatic control system. The future trend is moving towards decentralized control concepts including appropriate occupancy detection and space zoning. Simulation-based controls and learning systems are identified as appropriate methods that can play a decisive role in reducing building energy demand through integral control concepts.

Vision-based human activity recognition for reducing building energy demand

Occupancy behaviour in buildings can impact the energy performance and the operation of heating, ventilation and air-conditioning systems. To ensure building operations become optimised, it is vital to develop solutions that can monitor the utilisation of indoor spaces and provide occupants’ actual thermal comfort requirements. This study presents the analysis of the application of a vision-based deep learning approach for human activity detection and recognition in buildings. A convolutional neural network was employed to enable the detection and classification of occupancy activities. The model was deployed to a camera that enabled real-time detections, giving an average detection accuracy of 98.65%. Data on the number of occupants performing each of the selected activities were collected, and deep learning–influenced profile was generated. Building energy simulation and various scenario-based cases were used to assess the impact of such an approach on the building energy demand and provide insights into how the proposed detection method can enable heating, ventilation and air-conditioning systems to respond to occupancy’s dynamic changes. Results indicated that the deep learning approach could reduce the over- or under-estimation of occupancy heat gains. It is envisioned that the approach can be coupled with heating, ventilation and air-conditioning controls to adjust the setpoint based on the building space’s actual requirements, which could provide more comfortable environments and minimise unnecessary building energy loads. Practical application Occupancy behaviour has been identified as an important issue impacting the energy demand of building and heating, ventilation and air-conditioning systems. This study proposes a vision-based deep learning approach to capture, detect and recognise in real-time the occupancy patterns and activities within an office space environment. Initial building energy simulation analysis of the application of such an approach within buildings was performed. The proposed approach is envisioned to enable heating, ventilation and air-conditioning systems to adapt and make a timely response based on occupancy’s dynamic changes. The results presented here show the practicality of such an approach that could be integrated with heating, ventilation and air-conditioning systems for various building spaces and environments.

A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand

Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand

Vision-based detection and prediction of equipment heat gains in commercial office buildings using a deep learning method

Building energy consumption accounts for a large proportion of energy use globally. Previous works have shown that a large amount of energy is wasted in under- or over-utilized spaces since typicalbuilding management systems function based on fixed or static operation schedules. While the presence of occupants and how they use equipment contribute to the internal energy demand and affect the thermal environment. Office buildings are likely to have higher cooling demands in the future due toincreasing use of equipment, emphasising the need to develop systems which can better understand (and reduce) the impact of internal gains from equipmentand adapt to actualrequirements. This project aims to develop a deep learning-based approach which enables the detection and recognition of equipment usage and the associated heat emissions in office spaces. Subsequently, the data can be fed into building energy management systems through the formation of equipment heat gain profile; therefore, building energy usage can be effectively managed. Experiments were conducted in typical offices to generate the corresponding heat gain profiles, and then these were used in building simulation software to assess building performance. It was found that the model can perform equipment detection with an accuracy of 89.3%. While maintaining thermal comfort levels, up to 19% annual cooling energy demand reduction can be achieved by the proposed strategy when compared to that for the building managed by a static scheduled heating, ventilation and air-conditioning system, where in the studies, we focus on three types of equipment - computer, printer and kettle that are widely used in the office buildings. The findings indicate that it is feasible to use the deep learning approach to predict equipment heat emission for achieving effective building energy management therefore to reduce building energy demand.

Combined Effect of Outdoor Microclimate Boundary Conditions on Air Conditioning System’s Efficiency and Building Energy Demand in Net Zero Energy Settlements

In Europe, near zero energy buildings (NZEBs) represent the new frontier of energy efficiency in the built environment. Shifting the scale from NZEBs to net zero energy (NZE) settlements represents the opportunity to achieve further energy, environmental, and cost benefits, thanks to shared energy management, optimization of renewable energy systems, and microclimate mitigation. In particular, the last aspect takes advantage of the implementation of local microclimate mitigation strategies at a larger scale to improve settlements outdoor environmental conditions and citizens’ wellbeing. Furthermore, this inter-building scale mitigation involves relatively less severe working boundary conditions for buildings, able to both reduce building energy demand and improve HVAC (heating, ventilation, and air conditioning) system’s efficiency. In this study, this potential extra-energy saving, achievable without additional costs, is explored within the framework of a NZE settlement built in Italy thanks to an ongoing Horizon 2020 project. Improved working conditions for the operating air conditioning system and building energy savings thanks to settlement scale opportunities for microclimate mitigation are assessed. Findings show how this effect represents a further non-negligible energy-efficient and environmentally sustainable benefit achievable through the design and construction of NZE settlements. Potential energy savings are up to 24% when considering the double effect of microclimate mitigation.

On the localised climate change mitigation potential of building facades

Within the bounds of climate change, the design of public spaces and buildings must contribute to the local thermal mitigation. In this context, there has been a considerable body of research emphasis on reducing the Urban Heat Island (UHI) phenomenon via the resurfacing of public spaces, streets and roofs, with vegetation and cool materials. At the time of writing, minor attention has been given to how buildings' façades could affect outdoor temperature ranges.Whereas facades are studied according to their capabilities of reducing building energy demand and grant indoor comfort, but scarce attention has been given to how their surfaces properties are affecting local microclimates.The proposed work thus assesses the thermal mitigation potential of façades properties such as windows to wall ratios and finishes attribute, such as reflectivity and emissivity. The study, starting from the definition and calibration of a Ladybug Tools' simulation workflow for the assessment of outdoor comfort conditions, simulates urban canyons located in three climate types (temperate warm, temperate cold, and tropical arid), parametrises façade types, along with canyon aspect ratios and ground thermal properties. By creating a hierarchy of importance of interventions between aspect ratios, ground and façade finishes, the work offers information useful to foster site-specific climate mitigation plans.

Relationship between operational energy and life cycle cost performance of high-rise office buildings

Reducing building energy demand plays a vital role in addressing the depletion of energy resources and energy-related environmental issues. Previous research identified the statistical correlations between embodied energy and costs of buildings. However, little research has explored the relationship between operational energy and life cycle cost performance of buildings. This paper aims to investigate the energy efficiency and cost effectiveness of energy saving measures (ESMs) and thereby identify the relationship between operational energy and life cycle cost performance of high-rise office buildings. Five types of ESMs with 32 scenarios were examined using a real-life 35-storey office building in Hong Kong as a typical high-rise high-density city. The five types of ESMs are (1) increase temperature setpoint, (2) adopt daylighting sensors and light-emitting diode lamps (3) replace with low-e glass, (4) upgrade heating, ventilation and air conditioning system, and (5) implement on-site renewable energy. The results discover a U-curve relationship between operational energy saving and initial investment cost of the five ESMs. Moreover, the results show a significant linear correlation between operational energy saving and net present value of the ESMs. Such relationship reveals trade-off and synergy effects largely existing between opertional energy and cost performance in improving energy efficiency of high-rise offic buildings. This research suggests that increasing indoor temperature setpoint should be prioritised in lowering operational energy. The results are also discussed drawing on the findings of previous studies in other city contexts. This research contributes a novel life cycle perspective to future systematic research into building energy and economics. The findings are important to building designers in their decision-making of reducing buildings’ operational energy consumption in a cost-effective way.

A novel method of building climate subdivision oriented by reducing building energy demand

Climatic zoning is an important tool in building energy policy and regulations, smaller and more homogeneous climate zones could help enhance the rationality of energy policymaking. In most countries, climate zoning is based on simplified climate models, and it is difficult to accurately represent the energy demand characteristics of buildings. Therefore, this paper proposes a novel climate subdivision method that tackles the complex relations between climate and building energy-efficient, which move from traditional weather-based approaches to a performance-based approach. Integrate the uncertainty analysis method and the building dynamic performance simulation to extract a novel statistically significant index as the basis for classification. The K-means clustering method and discriminant analysis method are used to obtain and verify the clustering results, respectively. Furthermore, the comparison with clustering results based on degree days confirms the superiority of the new method proposed in this paper in identifying building energy demand.The method is applied to the cold climate zone of China as a showcase. The results show significant differences in building energy demand and the order of key features affecting building performance between each region in the climatic zones. The zone can be further sub-divided into four groups considering the climate factor and building feature uncertainty, and clustering results have been verified by several evaluation indicators.