Detecting errors in short-term electricity demand forecast using people dynamics

The landscape of power grids is gradually changing. The growing number of electrical appliances as well as the outbreak of Electric Vehicles (EVs) is increasing the need for electricity. As a consequence, high-accuracy consumption predictions are necessary in order to both schedule and plan production and operations accordingly. The emergence of connected “tracking” devices opens up new data-sets into both Internet-of-Things and Big Data worlds. It provides information on human dynamics (people mobility behavior) and with it several opportunities. Electricity consumption is impacted by people movements as while moving they are not “connected” to the power grid. Therefore, predicting such movement patterns and volume could help electricity providers improve their own consumption predictions. This paper presents a system and methods used to predict people movement behaviors as well as detect any anomaly. A scoring system is used to both evaluate the dynamics predictions and raise alerts when the computed score surpasses established thresholds. This proposal is tested over a scenario using available datasets and demonstrates that modifications in people movement behavior is affecting the consumption profile.