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Solar radiation is an essential meteorological parameter for building energy efficiency analysis, and the quality of the data directly affects the analysis results. This paper investigates the estimation of hourly solar radiation based on the generation of the typical meteorological year(TMY) using various real meteorological parameters and limited solar radiation data. The focus of this paper is to use two types of neural network algorithms to improve the estimation accuracy and applicability, and to solve the problem of hourly solar radiation data acquisition in non-radiation areas. First, select two city station data and use three methods to generate TMY. Then, two neural network models, BP Neural Network (BP)，Convolutional Neural Network (CNN) are used to estimate the hourly solar radiation data and verify the results. Finally, by constructing a photovoltaic-integrated office building model, the accuracy of the hourly solar radiation estimation model is verified using energy consumption simulation and photovoltaic (PV) power generation simulation. The results show that this paper can well solve the problem of limited radiation data, which provides a new idea for the study of building energy efficiency in areas where radiation data is missing. • Multiple neural network is used to estimate the generation of solar radiation data using other meteorological parameters. • An advanced neural network is used to provide an effective TMY generation method in missing hourly solar radiation data. • The accuracy of the experimental results is verified by energy consumption simulation and photovoltaic power generation. [ABSTRACT FROM AUTHOR]

The building sector accounts for nearly 1/3 of global energy end-use and associated carbon emissions. Fulfilling buildings' thermal demand in an efficient way is critical for decarbonizing the building sector. Therefore, this paper introduces the thermally activated building system (TABS) as an energy retrofit solution. A TABS floor-by-floor return water temperature control approach is proposed to reduce buildings' energy use and carbon emissions. The approach includes the topology optimization of TABS and flow control. The bypass pipe connections of the TABS to each floor, namely, the floor-by-floor pipe network topology, have been optimized by using genetic algorithms to achieve the lowest total cost. A case study of a high-rise building located in Beijing demonstrates that the proposed approach can improve the system's energy efficiency compared to the conventional TABS without optimization. The reduction of energy cost can be up to 11.21% with a 13.53% annual emission reduction. In general, the proposed TABS floor-by-floor return water temperature control approach can significantly improve the building efficiency comprehensively, therefore can be considered as a promising energy retrofit solution for the decarbonization of high-rise buildings. [Display omitted] • Fulfill building's thermal demand by TABS – an efficient way with less emissions. • Propose a floor-by-floor return water temperature control approach of TABS. • Proposed method can reduce energy use and emissions even without optimization. • Optimize the topology of TABS by using genetic algorithm. • Optimized TABS can save 11.21% energy cost and reduce 13.53% emissions. [ABSTRACT FROM AUTHOR]

Urban building energy modelling (UBEM) for analysing buildings in their spatial and functional context is an arising method. Only a few UBEM procedures use detailed building simulation tools, which are essential for high temporal and spatial resolution. This paper aims at developing a detailed automated physical bottom-up UBEM framework based on archetypes using Energy Performance Certificate data for predicting hourly heat load profiles of residential buildings. Simulation results are compared to and validated with measurements of two district heating networks and values from the TABULA typology. A comparison of the simulated hourly heat load profile for space heating and domestic hot water with measurement data results in a CV(RMSE) of 0.3, NMBE of 0.085, R2 of 0.85 and r of 0.94 for a sample size of 66 residential buildings, solely based on an estimation of the 3 classification criteria of the archetypes (building period, building condition and building type) and an estimation of the conditioned gross floor area for each measured building. Hence, the model can be declared as calibrated according to acceptance criteria in literature. • Novel approach for predicting hourly heat load profiles of districts. • Using Energy Performance Certificate data for district modelling. • Including model calibration with measurement data for residential buildings. • Implementing meteorological grid data for considering local weather conditions. • Presenting a bottom-up, physical, detailed urban building energy modelling method. [ABSTRACT FROM AUTHOR]

The typical meteorological year (TMY) is one of the key factors to accurately estimate building energy consumption and analysis indoor environment. Therefore, high-performance TMY is crucial. A new TMY generation method based on empirical mode decomposition (EEMD) in this paper was put forward for different climates of China. The results show that the proposed method effectively improves the representation of TMY compared with traditional methods, especially for solar radiation and wind speed data. Finally, this paper analyzed the impact of TMY using EEMD on the results of building environmental analysis. The simulation result has showed that the TMY generated by the EEMD effectively reduced the relative average deviation for the estimation of the building energy consumption compared with other methods. This study further uses the method to obtain the daily weather database for selected cities in different climate regions of China. • A new typical meteorological year generation method is proposed. • Ensemble empirical mode decomposition was used for the development of TMY. • The TMY database for selected cities in different climate zones was obtained. • The performance comparison analysis of TMY with traditional methods was carried. • The impact of the new TMY on building efficiency and thermal comfort is analyzed. [ABSTRACT FROM AUTHOR]

The development of heat pump technology in the tertiary sector can lead to a reduction in greenhouse emissions and enhanced exploitation of renewable energy sources, including solar energy. The present paper investigates the potentiality of an energy system equipped with photovoltaic thermal (PV) hybrid solar collectors, a storage tanks for the heat source and a heat pump for the space heating of a small office located in three different European cities. The behaviour of the heat pump according to the heat taken from the photovoltaic panels and stored in the tank has been analysed. An energy analysis was performed according to the efficiency of the heat pump and then the thermodynamic parameters of the system were evaluated. The analytical model of each subsystem of the thermal plant was developed in Matlab where hourly simulation during the period of heating season were performed. Therefore, a comparison between the results obtained for Rome, Milan and Cracow provided the heating energy demand covering rate for each of the cities in different subsystem efficiency conditions. When thermal and electrical efficiency of PVT are set respectively to 0.6 and 0.15 and the efficiency of storage unit heat exchanger is considered equal to 0.9 the heating demand covered for Rome is 70%, for Milan is 62%, while for Cracow is 47%. Those values show a good potential for supporting heat-pump based PV-T heating systems. • A novel combined PV-T heat pump system for supporting building heating is proposed. • A mathematical model of the system is developed. • The system performance is studied for three different locations: Cracow, Milan and Rome. • The system heating efficiency is the highest in Rome, while the lowest in Cracow. [ABSTRACT FROM AUTHOR]

In the early architectural design and energy saving reconstruction of building, it is essential need the typical meteorological year（TMY）for building performance analysis. The accuracy of the typical meteorological year data is directly impact the energy simulation results. In order to obtain accurate TMY data for construction engineering applications, the paper does the following work: (1) Reviewed the progress of the typical meteorological year; (2) Several TMY generation methods were compared using the same raw weather data, discusses the applicability of these methods for the cities of typical climate zones in China. Based on the most recent and comprehensive first-hand ground observation data, this paper adopts three methods to generate TMY for the typical city covering the climate zoning of China. Result shows different TMY generation method has applicability in different region, and through the simulation of building energy consumption, the comprehensive method has obvious advantages. It shows that in a region with diverse regional climate characteristics such as China, one certain method for selecting TMY is not enough, and comprehensive methods should be proposed to meet the needs of different building types and energy consumption simulation. • The development of typical meteorological year is reviewed. • Meteorological data covering China's climate zones are analyzed. • Several methods for generating typical meteorological year are compared. • Applicability of TMY method under different climate characteristics is obtained. [ABSTRACT FROM AUTHOR]

Both new and existing buildings need to be adapted to climate change, in order to keep providing a comfortable and healthy indoor climate. Preferably, the adaptation measures applied at the building level scale do not require additional energy (i.e. passive measures). Previous studies showed that passive climate change adaptation measures can have a positive effect on thermal comfort in summer and its shoulder seasons in non-air-conditioned residential buildings. In this paper, the effect of these passive climate adaptation measures – applied at building component level – on the cooling and heating energy demand of a terraced house is analyzed using building energy simulations. It is shown that for this particular case the required cooling energy can be limited to a large extent (59–74%) when external solar shading or additional natural ventilation is applied. In addition, it is shown that for a well-insulated terraced house the energy cost for heating is not strongly affected by the application of passive climate change adaptation measures. [ABSTRACT FROM AUTHOR]

In a residential building, a balcony of an upper floor can act as an overhang; and provide solar shading and reduction in electricity consumption of air-conditioner (A/C) for a flat on the underneath floor. However, some residential flats located on the lower levels may receive substantial self-shading effect from some adjacent flats in the same building block, leading to an insignificant shading effect from a balcony. As there is substantial amount of energy consumed and pollutant generated during the production and disposal of a balcony, it is vital to investigate the energy and environmental performance of residential flats installed with balconies at various floor levels. The objective of this study is to investigate an appropriate floor level of a residential building above which balconies should be incorporated. A 21-story residential building was modeled using EnergyPlus. Simulation results indicated that, for a west-facing flat, only the flats located on 15/F to 20/F can give acceptable environmental payback periods, ranging from 58.3 years to 40.7 years, i.e. within the lifespan (60 years) of a building. The corresponding annual savings in A/C consumption range from 234.9 MJ (2.60%) to 336.7 MJ (3.57%). The research methodology and findings are presented in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a building energy simulation tool is exploited to study the impact of multiple radiative inter-reflections exchanges in an urban environment. The aim is to evaluate their influence on the thermal energy demand of buildings. A street canyon model validated in a previous work is used in TRNSYS to investigate the effects of the related urban radiative trapping. Due to multiple shortwave and longwave reflections, the actual radiation exchanged by the buildings facades is different if compared to a street canyon building, where only shadowing phenomena due to canyon geometry are considered. Buildings energy simulation commercial codes do not take in account inter-reflections inside urban canyons. The objective of this study is to evaluate how multiple shortwave and longwave reflections affect thermal energy demand (cooling and heating) of a street canyon building depending on its orientation, its transparent/opaque surfaces ratio and on the solar absorption factor of the envelope surfaces. Increases in cooling demand up to 50% and decreases in heating demand up to 20% are found. • Numerical model thought of a TRNSYS "false zone" of a street canyon was performed. • The effects of multiple shortwave and longwave inter-reflections exchanges between neighboring buildings were analysed. • The main purpose was the evaluation multiple shortwave and longwave reflections in an urban canyon. • Effect of the urban canyon on the thermal energy demand (cooling and heating) of a building was investigated. [ABSTRACT FROM AUTHOR]

It was found from monitored data from eight dwellings in a case study building in Quebec City (Canada) that there are clear differences in the window opening behavior between different households. This paper aims to develop from data a probabilistic window opening model that accounts for occupant behavior. Logit regression is employed to predict the state (opened/closed) of windows according to indoor and outdoor temperatures environmental and temporal parameters. To replicate the diversity of behavior, normal distribution functions applied to the logit regression coefficients are used so that simulated occupants respond differently to environmental stimuli. It was found that the model offers good prediction for the monitoring by only using the outdoor and indoor temperatures as predictors. The proposed methodology was tested by simulating 10,000 times a full validation year of the case study building and comparing the results with measured data. The agreement was good. The model overestimated slightly the total frequency of window opening in the dwellings and the number of window changes-of-state. A vast range of window opening behavior was generated by the model, showing its ability to reproduce both the aggregated window opening behavior and the diversity of behaviors of the case study building. • Strong variation of window opening behaviors are observed among 8 monitored dwellings. • Windows are opened on average 10% in winter and 36% in summer in these dwellings. • Logit regression with random weights can reproduce the observed variability. • Only indoor and outdoor temperatures are required as predictors in the model. • Number of events and their duration are the same between the model and dwellings. [ABSTRACT FROM AUTHOR]