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The use of machine learning techniques has been proven to be a viable solution for smart home energy management. These techniques autonomously control heating and domestic hot water systems, which are the most relevant loads in a dwelling, helping consumers to reduce energy consumption and also improving their comfort. Moreover, the number of houses equipped with renewable energy resources is increasing, and this is a key element for energy usage optimization, where coordinating loads and production can bring additional savings and reduce peak loads. In this regard, we propose the development of a deep reinforcement learning (DRL) algorithm for indoor and domestic hot water temperature control, aiming to reduce energy consumption by optimizing the usage of PV energy production. Furthermore, a methodology for a new dynamic indoor temperature setpoint definition is presented, thus …