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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]