Sustainability-Oriented Time-Varying Optimal Scheduling of Household Appliances Using Adaptive Multi-Source Transfer Algorithm

Under the “dual carbon” strategy, home energy management systems have emerged as a core demand-side management technology to enable sustainable residential energy consumption. Nevertheless, most existing research investigates appliance scheduling within static frameworks. This study develops a dynamic three-objective scheduling model that minimizes electricity cost, user dissatisfaction, and carbon emissions. An adaptive multi-source transfer learning-assisted dynamic multi-objective evolutionary algorithm (AMS-TL-RMMEDA) is proposed to track the time-varying Pareto-optimal sets. The algorithm assigns transfer weights using Hausdorff-based similarity metrics, transfers historical knee points via geodesic flow kernel subspace alignment, and adjusts its response strategy according to quantified change intensity. On DF1–DF14, AMS-TL-RMMEDA yields the best MIGD values for 8 out of 14 problems and attains the lowest Friedman rank of 1.98. In simulations with eight household appliances across three environmental drift patterns, its MIGD is 38.03–52.85% lower than that of the best competing algorithms. The obtained knee solutions reduce electricity cost by up to 16.6%, carbon emissions by up to 17.3%, and peak-to-average ratio by up to 12.5% at a modest comfort sacrifice. Accordingly, adaptive multi-source transfer learning guides residential energy use toward low-cost, low-carbon, and grid-friendly operation, offering a decision-support tool for sustainable home energy management.