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The application of phase change materials (PCMs) in building envelopes can help promote energy efficiency due to its high heat capacity. Our study aimed to provide energy and economic insights for deploying PCM to buildings in eight different regions of East Asia through a series of energy and economic analysis using computer modelling and simulations. The static payback period (SPP) and dynamic payback (DPP) methods were used to evaluate the economic feasibility of applying a PCM at different melting phase temperatures (20°C, 23°C, 25°C, 27°C and 29°C). Results show that the proper choice of a PCM melting temperature is a key factor to improve the performance of the PCM applied to buildings. A melting phase temperature of 29°C achieved the highest economic feasibility in Seoul, Tokyo; Pyongyang; Beijing; and Ulaanbaatar and a melting temperature of 23°C in Hong Kong had the highest economic feasibility. Overall, the combined economic and energy analysis presented in this study can play an important role in improving the energy and economic feasibility of PCM in buildings. [ABSTRACT FROM AUTHOR]

• The review focuses on energy prediction techniques for large-scale buildings. • Five types of energy demand determinants in large-scale buildings were analyzed. • The techniques are classified the black, white, and grey-box based approaches. • A variety of energy-related applications, including energy-mapping were analyzed. • The hybrid models are suitable for energy predictions in large-scale buildings. Building energy prediction techniques are the primary tool for moving towards sustainable built environments. Energy prediction models play irreplaceable roles in making energy policy and the development of the building sector. This paper presents a comprehensive review of the prevailing prediction techniques used in large-scale building energy applications under different scopes and different archetypes, including black-box, white-box, and grey-box based methods. Additionally, the advantages and disadvantages of the applications of those approaches are compared and discussed in the context of large-scale buildings. The review results show that prediction techniques have addressed a variety of large-scale building energy related-applications, such as energy consumption forecasting and prediction, energy consumption profiling, energy mapping and benchmarking of buildings. However, there are still some research gaps that require more attention such as the inclusion of occupant behavior in white-box based models and the explicit representation of end-uses in black-box based models. Significantly, this review concludes with a few key tasks for modification of the current prediction approach framework, which can help with forecasting future energy use changes of specific buildings during the retrofit process or inclusion of renewable energy technology. This would assist in developing an appropriate strategy for the sustainability of the built environment. [ABSTRACT FROM AUTHOR]

Development of energy prediction models plays an integral part in management and enhancement of the energy efficiency of buildings, including carbon emission reduction. Simplified and data-driven models are often the preferred option when detailed information of simulation is not available and the fast responses are required. This study developed data-driven models for predicting electricity and gas consumption in London's residential buildings at the middle super output areas (MSOA) and lower super output areas (LSOA) with multilayer neural network (MNN), multiple regression (MLR), random forest (RF), and gradient boosting (GB) algorithms, and factors related to socio-demographic, economic, and building characteristics were used as predictors. The results revealed that building characteristics, household income, and the number of households were the most important predictors of electricity and gas consumption. We also found that MNN models have outperformed MLR, RF and GB models in electricity and gas consumption prediction at MSOA and LSOA levels, with R2 values over 0.99 for the electricity consumption model. In summary, this study shows that the MNN models can be a useful tool to assist the formation of energy efficiency policies in buildings at MSOA and LSOA levels. • Four types of data-driven residential energy models at district scales in London were developed. • The performance of the developed models was validated against the independent testing data. • Multilayer neural network models outperformed the other three models. • Model inputs from UK census survey were effective in the model development. [ABSTRACT FROM AUTHOR]