Residential water use constitutes a major part of urban water demand, and has be gaining importance in the urban water supply. Considering the complexity of residential water use system, an agent-based social simulation, i.e. the Residential Water Use Model (RWUM), is developed in this paper to capture the behavioral characteristics of residential water usage. By disaggregating total water demands down to constituent end-uses, this model can evaluate heterogeneous consumer responses on water, taking into account the factors of market penetration of watersaving technologies, regulatory policies, economic development, as well as social consciousness and preferences. Also, uncertainty analysis technique is innovatively applied in this agent-based model for parameter calibration and model robust testing. According to the case study in Beijing, this model can provide insights to water management agency in evaluating different water usage polices, as well as estimations for potential water saving for future infrastructure development planning.

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Green infrastructure (GI) is a contemporary area of research worldwide, with the implementation of the findings alleviating issues globally. As a supplement and alternative to gray infrastructure, GI has multiple integrated benefits. Multi-objective GI optimization seeks to provide maximum integrated benefits. The purpose of this review is to highlight the integrated multifunctional effectiveness of GI and to summarize its multi-objective optimization methodology. Here, the multifunctional effectiveness of GI in hydrology, energy, climate, environment, ecology, and humanities as well as their interrelationships are summarized. Then, the main components of GI multi-objective optimization including the spatial scale application, optimization objectives, decision variables, optimization methods and optimization procedure as well as their relationships and mathematical representation are examined. However, certain challenges still exist. There is no consensus on how to measure and optimize the integrated multi-functional effectiveness of GI. Future research directions such as enhancing integrated multi-objective assessment and optimization, improving life cycle analysis and life cycle cost, integrating benefits of GI based on future uncertainties and developing integrated green–gray infrastructure are discussed. This is vital for improving its integrated multifunctional effectiveness and the final decision-making of stakeholders.

The abrupt outbreak of the COVID-19 pandemic was the most significant event in 2020, which had profound and lasting impacts across the world. Studies on energy markets observed a decline in energy demand and changes in energy consumption behaviors during COVID-19. However, as an essential part of system operation, how the load forecasting performs amid COVID-19 is not well understood. This paper aims to bridge the research gap by systematically evaluating models and features that can be used to improve the load forecasting performance amid COVID-19. Using real-world data from the New York Independent System Operator, our analysis employs three deep learning models and adopts both novel COVID-related features as well as classical weather-related features. We also propose simulating the stay-at-home situation with pre-stay-at-home weekend data and demonstrate its effectiveness in improving load forecasting accuracy during COVID-19. CCS CONCEPTS• Computing methodologies → Neural networks; • Hardware → Power and energy.

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