The performance and stability of organic–inorganic hybrid perovskite solar cells (PSCs) is sensitive to water and moisture in an ambient environment. Understanding how H₂O influences the perovskite material is also important for developing appropriate control strategies to mitigate the problem. Here we provide a comprehensive review on the effect of water on the state-of-the-art lead-based perovskite solar cells in terms of perovskite material design, perovskite film preparation, device fabrication, and photovoltaic application. It is found that a moderate amount of water can facilitate nucleation and crystallization of the perovskite material, resulting in better perovskite film quality and enhanced PSC performance. The perovskite materials are irreversibly destroyed by H₂O after a certain level of water, but they exihibit better tolerance than initially expected. Humidity resistant fabrication of high-performance PSC devices and modules should therefore be favoured. Generally, water shows a negative effect on the long-term stability and lifetime of PSCs. To reduce the effects from water during outdoor operation, attention should be paid to different protection methods such as varying the perovskite composition, optimizing the electron/hole transport layer and encapsulation of the device.

Electricity consumption data profiles that include details on the consumption can be generated with a bottom-up load models. In these models the load is constructed from elementary load components that can be households or even their individual appliances. In this work a simplified bottom-up model is presented. The model can be used to generate realistic domestic electricity consumption data on an hourly basis from a few up to thousands of households. The model uses input data that is available in public reports and statistics. Two measured data sets from block houses are also applied for statistical analysis, model training, and verification. Our analysis shows that the generated load profiles correlate well with real data. Furthermore, three case studies with generated load data demonstrate some opportunities for appliance level demand side management (DSM). With a mild DSM scheme using cold loads, the daily peak loads can be reduced 7.2% in average. With more severe DSM schemes the peak load at the yearly peak day can be completely levelled with 42% peak reduction and sudden 3 h loss of load can be compensated with 61% mean load reduction. Copyright © 2005 John Wiley & Sons, Ltd.