A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings

Smart buildings seek to have a balance between energy consumption and occupant comfort. To make this possible, smart buildings need to be able to foresee sudden changes in the building's energy consumption. With the help of forecasting models, building energy management systems, which are a fundamental part of smart buildings, know when sudden changes in the energy consumption pattern could occur. Currently, different forecasting methods use models that allow building energy management systems to forecast energy consumption. Due to this, it is increasingly necessary to have appropriate forecasting models to be able to maintain a balance between energy consumption and occupant comfort. The objective of this paper is to present an energy consumption forecasting strategy that allows hourly day-ahead predictions. The presented forecasting strategy is tested using real data from two buildings located in Valladolid, Spain. Different machine learning and deep learning models were used to analyze which could perform better with the proposed strategy. After establishing the performance of the models, a model was assembled using the mean of the prediction values of the top five models to obtain a model with better performance.

Automated summary

Smart buildings aim to balance energy consumption and occupant comfort by predicting sudden changes in energy consumption patterns. This paper introduces an energy consumption forecasting strategy for hourly day-ahead predictions, tested on two buildings in Valladolid, Spain. Various machine learning and deep learning models were analyzed to improve performance, with a model combining the top five models' average prediction values identified as the best-performing approach.