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This paper proposed a user-friendly and adaptive home intelligent agent with self-learning capability for optimal scheduling of smart home energy systems. The intelligent agent autonomously identifies model parameters based on system operation data, eliminating the need for manual input and making it more user-friendly and practical to implement. It can also self-learn the latest energy consumption information from an updated dataset and adaptively adjust model parameters to accommodate changing conditions. Utilizing these determined models as input, the intelligent agent performs day-ahead optimal scheduling using the proposed many-objective integer nonlinear optimization model and automatically controls system operation. Experimental studies were conducted on a laboratory-based smart home energy system to verify the effectiveness of the developed intelligent agent in different scenarios. The results consistently demonstrate Mean Absolute Percentage Errors below -12.7 % across all three scenarios, indicating the accuracy of the intelligent agent. Furthermore, the optimal scheduling significantly enhances system performances. After optimization, daily operational costs, peak-valley differences, and CO2 emissions were reduced by 34.1 % to 81.6 %, 29.2 % to 36.7 %, and 19.6 % to 43.2 %, respectively. Moreover, the PV generation self-consumption rate and self-sufficiency rate improved by 29.6 % to 38.0 % and 40.5 % to 49.4 %, respectively. The proposed intelligent agent provides invaluable guidance for optimal dispatch of smart home energy systems in real-world settings.

The optimal scheduling of home energy systems is influenced by the benefits of different stakeholders, with the hierarchical nature of user's needs being particularly significant. However, previous studies have largely neglected these factors. To bridge the research gaps, a many-objective optimal dispatch framework for home energy systems, which was inspired by Maslow's hierarchy of needs, was proposed. In the framework, user's needs for the optimal dispatch of home energy systems were categorized into various hierarchies referring to the Maslow's theory, which were fulfilled in a specific sequence during the scheduling optimization. In addition to the user's needs, the benefits of grid operators and policymakers were considered in the developed many-objective nonlinear optimal model, which includes six objective functions that capture the interests of end-users, grid operators, and policymakers. Simulation results obtained across the home energy systems with various configurations verified the effectiveness of the proposed framework. Results indicate that user's needs can be fully satisfied and a tradeoff among the benefits of end-users, grid operators, and policymakers was achieved. For various home energy systems, the optimal scheduling demonstrated reductions of 22.33 %-81.05 % in daily operation costs, 14.39 %-25.68 % in CO2 emissions, and 15.58 %-17.49 % in peak-valley differences, associated with increment of 5.37 %-15.51 % in self-consumption rate and 8.91 %-27.29 % in self-sufficiency rate, compared with the benchmark. The proposed framework provides valuable guidance for the optimal scheduling of various home energy systems in practical applications.