Lighting, heating, and air conditioning systems are instances of how electricity use at buildings is of key importance for occupants comfort and well-being. Since the electricity can be produced but cannot be stored, for utility companies it is important to reliably forecast energy supply almost in near real-time. Nowadays, smart grid technologies development also require a high resolution forecasting to eliminate blackouts and to optimally adapt energy supply to customers’ needs. These are the reasons why the finest Machine Learning and Data Science based methods have been developed and applied to approach as much accurate as possible predictive models for short-term electricity consumption. This paper proposes to enhance those predictive models by using weather and calendar information to configure a more complete working database. In addition, a cluster-based forecasting methodology will augment any predictive model with learning from other buildings. Thus, predicting future values for one smart meter is approached by utilising not only its own historical electricity consumption values, but working with a multivariate time series on weather and calendar data and information from other buildings at the same cluster. This proposal has been tested with measures from smart meters collected every 30 minutes during one year for 5 selected buildings in Bristol (UK). The enhancing methodology can predict electricity consumption data with higher accuracy than using data from just one building
