Distributed battery energy storage systems (BESS) have become increasingly popular to reduce utility costs, to mitigate the mismatch between uncontrollable renewable generation and electricity demand, and therefore to improve grid stability. However, determining the optimal BESS capacity remains a complex challenge, especially for large-scale industrial parks with distributed renewable energy systems. Existing approaches typically rely on Mixed-Integer Linear Programming approaches and nonlinear economic battery models, which create significant bottlenecks for computational efficiency and financial evaluation. To tackle these problems, this paper proposed a computationally efficient Linear Programming optimization framework and developed an off-the-shelf software tool for optimal BESS sizing in large-scale industrial parks. A key contribution of this work is the development of a linearized battery life-cycle cost model that embeds self-degradation rates directly into the Capital Recovery Factor, avoiding the computational burden of non-linear degradation models while ensuring economic accuracy. A comprehensive case study is conducted at a real-world industrial park located in Changsha, China, to verify the performance of the developed software and investigate the impact of multiple factors on the optimal BESS capacity. The results demonstrate that the developed Linear Programming framework identifies the maximum profitability bound for BESS more accurately than rule-based control framework and operates with significantly higher computational efficiency than Mixed-Integer Linear Programming models. Furthermore, the synergistic effect between the BESS and photovoltaic systems is also identified, revealing that their combined deployment yields a payback period of 4.56 years, thereby providing valuable quantitative insights for the optimal design of distributed energy systems.

Large language models (LLMs) exhibit significant potential in automating data-driven building load forecasting (BLF) model development, substantially reducing reliance on human effort and domain expertise. However, direct application of LLMs faces challenges, including the large and indivisible nature of optimization problems, slow optimization, error-prone code generation, and underutilization of LLM reasoning capabilities. This study introduces AutoLFM, a novel multi-agent framework leveraging LLMs to automate the end-to-end BLF model development workflow. AutoLFM decomposes the complex modeling process using a two-stage optimization strategy, with specialized LLM agents (Retriever, Reasoner, Coder, and Validator) performing distinct tasks. Key mechanisms include dynamic knowledge retrieval for prompt enhancement, data-adaptive search space generation by the Reasoner, and verification-enhanced code generation between the Coder and Validator. Experimental evaluations on three real-world building datasets demonstrate that AutoLFM efficiently generates BLF models, achieving predictive accuracy comparable to or exceeding manually designed baselines and other LLM-based methods, with an average R² improvement of 12.3%. It shortens the traditional development cycle from weeks to hours while achieving a 100% code generation success rate. Ablation study confirms the contributions of two-stage optimization, data-adaptive search space, and validation-enhanced code generation to the framework's performance and reliability. AutoLFM highlights the potential of multi-agent LLM systems in automating complex time-series forecasting tasks, significantly reducing development time and dependence on specialized knowledge.

Building simulation based on physical modeling is commonly adopted for performance prediction, but the high time costs hinder its application in the early design stage of buildings. Data-driven surrogate models have been proposed as a means to replicate computationally expensive simulation models. However, existing surrogate models for sustainable residential block design are limited in scope, focusing either on individual buildings or on specific cases within multi-block projects. This study leverages graph neural networks to develop optimal surrogate models incorporating inter-building effects to predict multiple indicators of sustainable performance for residential blocks at a region level. A graph schema is proposed to represent the general geometric features and relations among buildings in residential block design. A regional dataset is generated for model training and testing, using real residential zones in Hong Kong. The surrogate models are developed and evaluated, using two kinds of architectures (individual architectures for specific indicators and an integrative architecture) and three different neural networks (graph attention network (GAT), graph convolutional network, and artificial neural network). The results showed that the surrogate models using the individual architectures and GAT outperform the models using other architectures and neural networks. These surrogate models achieve a high prediction accuracy with CV(RMSE)s of 11.79%, 7.63%, and 8.00% in terms of energy consumption, indoor thermal comfort, and daylighting, respectively, on the regional test set. Moreover, they enable a significant acceleration of the performance evaluation, reducing the calculation time from 6.346 min to 1.565 ms (243,297 times) per case compared to physics-based simulations.

Environmental monitoring plays a critical role in creating and maintaining a comfortable, productive, and healthy environment. Built upon the advancements of robotics and data processing, mobile sensing demonstrates its potential to address problems regarding cost, deployment, and resolution that stationary monitoring encounters, which therefore has attracted increasing research attentions recently. To facilitate mobile sensing, two key algorithms are needed: the field reconstruction algorithm and the route planning algorithm. The field reconstruction algorithm is to reconstruct the entire environment field from spatially- and temporally-discrete measurements collected by the mobile sensors. The route planning algorithm is to instruct the mobile sensors where the mobile sensor needs to move to for the next measurements. The performance of mobile sensors highly depends on these two algorithms. However, developing and testing those algorithms in the real world is expensive, challenging, and time-consuming. To address these issues, we proposed and implemented an open-source virtual testbed, AlphaMobileSensing, that can be used to develop, test, and benchmark mobile sensing algorithms. AlphaMobileSensing aims to help users more easily develop and test the field reconstruction and route planning algorithms for mobile sensing solutions, without worrying about hardware fault, test accidents (such as collision during the test), etc. The separation of concerns can significantly reduce the cost of developing software solutions for mobile sensing. For versatility and flexibility, AlphaMobileSensing was wrapped up using the standardized interface of OpenAI Gym, and it also provides an interface for loading physical fields that were generated by numerical simulations as virtual test sites to perform mobile sensing and retrieving monitoring data. We demonstrated applications of the virtual testbed by implementing and testing algorithms for physical field reconstruction in both static and dynamic indoor thermal environments. AlphaMobileSensing provides a novel and flexible platform to develop, test, and benchmark mobile sensing algorithms more easily, conveniently, and efficiently.

AlphaMobileSensing is open sourced at https://github.com/kishuqizhou/AlphaMobileSensing.