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Energy conservation in residential buildings has been a topic of interest in recent years because of their high levels of energy consumption. Weatherization is set of approaches that can be used to make buildings more energy-efficient, thereby helping residents lower their energy bills and improving environmental sustainability. However, there are two significant challenges associated with weatherization adoption: high upfront investment costs with a long payback period, and minimal awareness of weatherization and its benefits. This paper proposes an agent-based model that will allow researchers to explore residents' socially-motivated energy conservation decisions by providing a realistic social context via a multilayer social network and incorporating opinion dynamics based on the Susceptible-Exposed-Infected-Recovered epidemic model. Several experimental scenarios are run to demonstrate the model's potential to help policymakers determine how to encourage residential weatherization adoption.

The electricity market in the U.S. is changing rapidly from a utility-scale centralized generation-distribution model to a more distributed and customer-sited energy model. Increasingly, residential consumers are showing interest in solar-based electricity, which has resulted in increased adoption of distributed solar on the rooftops of owner-occupied residences. However, limited accessibility of rooftop photovoltaic (PV) has led to equity concerns among policymakers. Also, utility companies face a decline in revenues as more residents adopt rooftop PV. In response to these issues, utility companies must consider providing alternative renewable energy options to their customers and incorporate consumer adoption modeling in their expansion planning approach. Agent-based modeling enables energy consumers' socially-motivated adoption decisions to be realistically captured. This paper describes an agent-based model that demonstrates the value of incorporating consumer-adoption modeling in a utility company's expansion planning approach.

Dynamic symbolic execution is an important automated testing technique. Firstly, we introduce the traditional symbolic execution and dynamic symbolic execution technology, and then review the research status of Python application testing with dynamic symbolic execution technology. Then we compare and analyze the dynamic symbolic execution tools with the existing Python programs and Architectures, performance, and supported data types. Finally, the development direction of this field prospects. It is helpful for researchers to understand the progress of dynamic symbolic execution technology in current Python program and lay a foundation for future research work.

This work surveyed residents of an economically disadvantaged community on their attitudes toward weatherization and their energy use behaviors. To support urban leaders making decisions to mitigate the effects of large-scale climate change, data-driven simulation models are being developed. To ensure that these models are equitable, the needs of all citizens must be included, especially those most vulnerable to the impacts of climate change. The results of this survey indicate that residents are taking steps to weatherize and conserve energy, but they are hindered by a lack of resources and knowledge of available assistance programs. These results are being applied to agent-based models (ABM) to model emergent community behavior. These models will be used to design decision support systems for city stakeholders.