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• Automatic MiC envelopes and layout generation by a novel SSG-MOO method. • Multi-objective optimization formulation for passive MiC design. • A pilot MiC study in Hong Kong produced 5 selected Pareto optima out of 625. • Up to 0.42% energy savings and 9.71% spatial daylight autonomy improvement against the baseline. • A multi-level analysis of results and design strategies for practitioners. Modular-integrated Construction (MiC) is an emerging construction technique promoted in the building sector for high productivity and low waste emission in the construction phase; yet, the standardized modules also bring new challenges, such as balancing passive energy efficiency and spatial daylight autonomy, to the operational phase. This paper proposes a Symmetric Skeleton Grammar-based Multi-Objective Optimization (SSG-MOO) method to formulate parametric MiC envelopes and detailed layout, with the two objective functions being energy efficiency and interior daylight performance in the operational phase. Pareto optima of the SSG-MOO, computed by the Non-dominated Sorting Genetic Algorithm II, are generally verified and analyzed in three levels, i.e., MOO's solution space, SSG layout, and MiC design parameters. A case study of MiC residential building in Hong Kong demonstrated the SSG-MOO method through five new passive MiC designs (i.e., spatial reorganization of three architectural modules, and parameter tuning of the envelops and corridors), achieving up to 0.42% energy savings and 9.71% spatial daylight autonomy improvement compared to the baseline design. The contribution of this paper is two-fold, including a novel and sound SSG-MOO formulation for parametric MiC designs, and offering time-efficient and evidence-based design tactics for MiC designers and industrial practitioners to push boundaries of MiC. [ABSTRACT FROM AUTHOR]

• A data-informed building energy management (DiBEM) framework was proposed. • It was demonstrated in an office/laboratory building with two-years' experimental data. • The impacts of energy efficient interventions were quantified through data-driven models. • The energy use intensity was reduced from 54.1 kWh/m2 to 42.8 kWh/m2. To support building operations in reaching ultra-low energy targets, this paper proposes a data-informed building energy management (DiBEM) framework to improve energy efficiency systematically and continuously at the operation stage. Specifically, it has two key features including data-informed energy-saving potential identification and data-driven model-based energy savings evaluation. The paper demonstrates the proposed DiBEM with a detailed case study of an office and living laboratory building located in Cambridge, Massachusetts called HouseZero. It focuses on revealing the performance of the energy-efficient interventions from two-years' building performance monitoring data, as well as evaluating energy savings from the interventions based on the data-driven approach. With Year 1 as baseline, several interventions are proposed for Year 2 including improvements to controls and operation settings, encouragement of occupants' behavior for energy savings, and hardware retrofitting. These were deployed to heating/cooling, domestic hot water, lighting, plug and other loads, and photovoltaic (PV) systems. To quantify the impacts of different interventions on energy end uses, several data-driven models are developed. These models utilize linear regression, condition model, and machine learning techniques. Consequently, the heating/cooling energy consumption that was already ultra-low in Year 1 (12.8 kWh/m2) is further reduced to 9.7 kWh/m2 in Year 2, while the indoor thermal environment is well maintained. The domestic hot water energy is reduced from 2.3 kWh/m2 to 1.2 kWh/m2. The lighting energy is only increased from 0.3 kWh/m2 in pandemic operations without occupancy in Year 1 to 0.8 kWh/m2 in partial normal operations in Year 2, while the indoor illuminance level meets occupants' requirements. Combined with other relatively constant loads and the reduction of plug and other loads due to COVID building operation restrictions, the total energy use intensity is thereby reduced from 54.1 kWh/m2 to 42.8 kWh/m2, where 5.4 kWh/m2 of energy reduction for Year 2 is estimated to be contributed by the energy-efficient interventions. PV generation is 36.1 kWh/m2, with an increase of 1.4 kWh/m2 from a new inverter. In summary, this paper demonstrates the use of DiBEM through a detailed case study and long-term monitoring data as evidence to achieve ultra-low energy operations. [ABSTRACT FROM AUTHOR]

Facing the challage of energy conservation and emission reduction in the building sector, most energy saving strategies of central heating only focus on the performance improvement of building envelope, ignoring the impact of energy use behavior. This paper proposed a building energy conservation strategy based on energy use behavior analysis, which provides a perspective for in-depth exploration of building energy conservation. After investigating a typical university in the northern China, the energy use behaviors were characterized and two different DeST energy consumption models before or after considering energy use behavior were established. By comparing the results calculated by these two models, the impact of energy use behavior analysis on building energy consumption was quantitatively analyzed. The results show that the heating energy consumption and carbon emission can be reduced by 23.28% and 23% respectively through the proposed strategy based on energy use behavior. Therefore, the analysis of energy use behavior can provide a theoretical basis for heating operators to make decisions on energy supply regulation. With central cooling and heating further popularizing in the future, the research methodology of this paper has more extensive reference value for energy conservation and emission reduction strategies in the building sector. [ABSTRACT FROM AUTHOR]

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions. • Recommender systems for energy efficiency in buildings. • Taxonomy of existing energy saving recommender systems. • Explainable and fusion-based recommender systems. • Visualization recommendation systems. [ABSTRACT FROM AUTHOR]

• Obtaining the optimal interpolation method for adjacent sites. • Proposing the method for revising meteorological data at city scale. • Verifying the accuracy of the method by energy consumption simulation. In the development process of building energy conservation design, the correct provision of local Typical Meteorological Year (TMY) data is a prerequisite and basic work to evaluate building energy consumption, which directly affects the fine use of outdoor data for building energy conservation. Based on the recorded and observed data of meteorological stations in China, the acquisition and correction methods of TMY were studied by mathematical statistics and target searching, and the building energy consumption simulation of urban office buildings was conducted to verify the accuracy of the method, including:(1) Reviewed the generation method for TMY and the acquisition of data.(2) Based on the existing TMY data, the best spatial interpolation method of adjacent stations is studied by using the standard deviation method, and the selection method of the best number of stations on a large regional scale is given.(3) An improved method of urban environment TMY is proposed. The dry bulb temperature and relative humidity of the building site are corrected to generate TMY under Urban Heat Island (UHI) effect. The research in this paper improves the coverage and accuracy of urban TMY data and supplies basic research support for building energy-saving designs. [ABSTRACT FROM AUTHOR]

• First reviews on coupling utilisation of thermal sources in vapour compression heat pumps. • The existing thermal source coupling attempts in applied energy fields are summarised and compared. • Design features and performance enhancement principles are analysed in detail. • Recommendations for research and marketisation of energy source coupling methods are made. Vapour compression heat pump provides efficient cooling and heating through the use of various renewable energy sources and plays a significant role in the energy conservation of modern buildings. Its performance enhancement gains increasing attention under the background of conventional energy and environmental crisis. Since the efficiency of a heat pump cycle is closely related to its thermal source condition, a stable high-temperature heat source or low-temperature cold source could benefit its heating or cooling efficiency a lot. Following this improvement strategy, significant progress has been made over the past years on the use of different renewable energy sources as coupled thermal sources of heat pumps. However, a specialised review that summarises relevant studies remains to be published. This paper aims at presenting operating mechanisms, characteristics, benefits, and limitations of different energy source coupling methods for heat pump performance enhancement towards its building application. The most concerning research on solar-assisted air source heat pumps, solar-assisted ground source heat pumps, and air-ground dual source heat pumps is emphasised. The existing thermal source coupling attempts under other renewable energy fields, such as waste heat, biomass, and water sources, are also summarised. Recommendations for further research and marketisation of such technologies are made based on detailed performance characterisation and comparison. The review is expected to benefit researchers in heat pumps and relevant applied energy fields to understand the challenges and opportunities in thermal source coupling technology and to promote its research and application development. [ABSTRACT FROM AUTHOR]

• A kNN-BS method is proposed to repair lighting and plug energy data. • Using cross-validation method to select the optimal k. • Using 2-step data classification method to decide day type. • Achieved higher accuracy than traditional k NN. The public building energy consumption monitoring platforms (BECMPs) play an important role in building energy conservation and energy-related decision-making. However, problems such as data missing and data mutation are quite common that they impede the effectiveness of the BECMPs. Based on the k NN algorithm, this paper proposes a k NN-BS data repair algorithm with the characteristics of electricity consumption trends taken into consideration. After data classification and cleaning, the data of hourly lighting and plug power consumption of an office building of Dalian University of Technology is repaired using the k NN-BS algorithm. The cross-validation method is used to determine the optimal k value. The relative repair error of the total daily electricity consumption of k NN-BS is less than 4% for working days and less than 6% for non-working days. Compared with k NN, the average CVRMSE of the k NN-BS algorithm is reduced by 1.36%∼1.59% for working days and 5.49%∼8.17% for non-working days. The k NN-BS algorithm shows excellent stability and robustness compared with k NN, polynomial and BPNN, which makes it very suitable for practical use, especially in efficiency-demanding scenarios. The k NN-BS algorithm can effectively improve the quality of public building energy consumption monitoring data and provide a theoretical basis and technical means for solving data quality problems in the BECMPs. [ABSTRACT FROM AUTHOR]

• The outdoor environment indexes are integrated by simulation and empirical method. • Quantitatively study the interaction between green space and water bodies. • The proper layout of landscape planning can reduce the building carbon emissions. The rapid urban construction has made urban residential buildings become the main source of greenhouse gas emissions. So far, the existing studies on the impact of urban micro-climate on building energy carbon emissions have shown that green space (GS) and water bodies (WB) play positive roles in regulating outdoor thermal environment and reducing building energy consumption. Accordingly, this paper quantitatively explores the influence of the interaction between GS and WB on reducing building carbon emissions. First, the building's micro-climate is simulated by Computational Fluid Dynamics (CFD) to obtain outdoor thermal environment indexes, which are used as inputting data to calculate carbon emissions by software HDY-SMAD. Then, we utilize Geographic Information System (GIS) and Remote Sensing (RS) techniques to explore the natural surroundings of buildings, such as GS and WB distribution within the administrative scope of Dujiangyan. Next, the linear regression method is applied to analyze the relationship between GS, WB, and residential building carbon emissions, in which the residential carbon emissions data is obtained by social survey. Finally, the simulation results are verified by empirical research method. The findings demonstrate that the GS and WB have area, accessibility and seasonal differences in regulating local thermal environment and reducing building carbon emissions, and the overall regulation ability of WB for micro-climate is more significant than that of GS. In addition, the ratio area between GS and WB has the most significant influence on reducing building carbon emissions when it is 3:1 in winter, but this optimal ratio is 1:2 in summer. These conclusions can provide benchmark information for landscape planning and building energy conservation in the future. [ABSTRACT FROM AUTHOR]

• Encouraging decision-makers to rely on residential tree allocation to transform urban canopy cover target strategies. • Understanding the hierarchy of tree planting parameters, based upon minimum tree number and maximum energy efficiency. • Guaranteed winter optimal residential tree arrangement in conserving annual energy demands, even with evergreen tree planting. • Demonstrating immediate green settings absence in the NatHERS assessment package. • Recommendation of five optimal tree allocation depending upon deep soil availabilities. Energy–efficient dwellings promote substantial urban energy conservation. Residential tree allocation, as an Urban Heat Island (UHI) mitigation strategy, stimulates climate responsivity, lowers Air Conditioner (AC) usage and heat distribution. This paper evaluates dwellings' response to residential tree planting parameters to assess the building–surround relationship. These parameters include tree type (evergreen or deciduous), volume (1–3 trees), Tree–Building distance (3 m or 5 m) in each cardinal and inter–cardinal azimuth. These planting configurations highlight dominant Australian urban planning policy and green open space restrictions. This study quantifies tree planting configuration models, utilising both typical and extreme weather data and a bi–seasonal approach, to arrive at an Optimal Residential Tree arrangement (ORTa). The simulation process tailors local weather data to assess tree impact upon the diurnal and nocturnal microclimate. The ORTa dependence upon orientation ensures correct deep soil levels and viable private backyard volume. The result demonstrates a high probability that deciduous trees save energy bi–seasonally. Across all aspects and weather conditions, ORTa allows for an evergreen tree addition to boost energy conservation without detrimental annual or bi–seasonal effects on building thermal response. Annual weather data indicates two deciduous trees are optimal. These trees would be located east or west at 3 m Tree–Building distance (T-B distance), north at 5 m T-B distance or south at 3 m minimum with the maximum depending upon neighbour's northerly aspect. In typical weather conditions, two deciduous tree arrangements lead to maximum 40% heating energy conservation from any potential ORTa in east, west or north. In addition, it provides 15% east or west cooling energy saving and 7% north. In extreme weather conditions, two tree arrangements provide 25% thermal heating conservation, in any aspect. During heatwaves easterly deciduous tree planting is optimal (18% energy conservation), followed by westerly (7%) and northerly (1%). This research recommends five ranked optimal tree arrangements depending on residential parcel deep soil availabilities. This optimisation result encourages decision–makers to appreciate residential green space and reanalyse future urban canopy cover target measurements. [ABSTRACT FROM AUTHOR]