The need for electricity has never been more important these days. In order to achieve balance between generation and distribution - as well as schedule operations accordingly-high-accuracy load demand predictions are mandatory. But our society is currently undergoing modifications in electricity consumption allocation. We are witnessing a fast shift from office-to home- based working style. As a consequence, Electricity load demand prediction models are in need for additional data in order to quickly adapt to these modifications and maintain efficient predictions accuracy. The rising popularity of "tracking" devices and alike-applications opens up to a new type of multi-modal investigations. The availability of associated location data enables researcher to study mobility routine and patterns in order to cross it with other data. Electricity consumption is one domain impacted by people's mobility behavior (commuting, telework, etc.) as people are not "plugged" onto the power grid while moving. This paper presents a study on population staying patterns and how it can relate to electricity load demand. Time-series data providing the number of people staying in a given area has been used within a Deep Learning model in order to enhance electricity load demand predictions at the provider level. It unveils the potential usage of such dynamics data, while setting the foundations for more complex studies.