This study presents an adaptive dynamic programming (ADP)-based approach for optimizing residential energy management, targeting both cost reduction and battery longevity. A model of the home energy system is first developed along with a corresponding objective function. The ADP technique is then applied to determine the optimal energy scheduling strategy. To facilitate practical application, the value function is approximated using a neural network. This study’s approach excludes the action network, discretizes the control over its allowable range, evaluates the performance index through exhaustive search, and chooses the control strategy that yields the lowest performance index. This method reduces the computational complexity. The numerical experiments verified the effectiveness.