The increasing penetration of renewable energy sources and the electrification of residential load have amplified volatility in distribution networks. This paper investigates the influence of human behavior on household electricity usage and investigates how smartphone-collected data can enhance single-household consumption forecasting. By incorporating features related to human activity into a state-of-the-art sequence-to-sequence model based on bidirectional long short-term memory (LSTM) with attention, prediction accuracy improves by 15–22%. Our findings highlight that behavioral information, such as app usage patterns, can significantly improve forecasting performance for five-hour-ahead or longer horizons, but also raise important privacy considerations that must be addressed.