Consumer behavior and habits play a crucial role in household energy consumption patterns. Influencing user behaviors towards sustainable electricity consumption practices consists an open challenge. To address this issue, the Internet of Behaviors (IoB) has emerged as a new paradigm that combines real-time data coming from Internet of Things (IoT) devices with information gathered from behavioral science and data analytics to influence people’s behavior. In the energy sector, IoB systems can build highly personalized models that allow smart home devices to encourage users to adopt more sustainable energy behaviours. This paper proposes a hybrid forecasting approach combining Long Short-Term Memory (LSTM) with a Transformer model to accurately predict electricity consumption in individual households. The proposed approach is integrated into a new IoB system designed to provide personalized and timely alerts that encourage energy-efficient practices, reducing thus costs and energy waste. The performance of this approach are compared with several baseline models using a real dataset related to household electricity consumption. The results show that the hybrid approach achieved lower Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) across various electricity patterns, demonstrating a better ability to anticipate future energy demands. The improved forecasting accuracy enables the IoB system to generate more precise and timely alerts, potentially leading to more effective user be- haviors influence and significant energy savings.