Machine Learning Algorithms for Short-Term Load Forecast in Residential Buildings Using Smart Meters, Sensors and Big Data Solutions

In this paper, we propose a scalable Big Data framework that collects the data from smart meters and weather sensors, pre-processes and loads it into a NoSQL database that is capable to store and further process large volumes of heterogeneous data. Then, a set of Machine Learning (ML) algorithms are designed and implemented to determine the load profiles and forecast the electricity consumption for residential buildings for the next 24 hours. For the Short-Term Load Forecast (STLF), a Feed-Forward Artificial Neural Network (FF-ANN) algorithm with backtracking adjustment of the learning rate that extends and optimizes the Nesterov learning method is proposed. Its performance is compared with six algorithms, i.e. FF-ANN with well-known learning methods, namely Momentum and Nesterov, Non-linear AutoRegressive with eXogenous (NARX), Deep Neural Network (DNN), Gradient Tree Boosting (GTB) and Random Forests (RF) that are competitive and powerful ML algorithms which have been successfully used for load forecast. Hence, for STLF, the seven algorithms are executed simultaneously and the best one is automatically selected considering its accuracy in terms of Root Mean Square Errors (RMSE). The proposed methodology contains the steps required to implement the Big Data framework, i.e. data pre-processing, transformation and loading, the configuration of the ML algorithms for dimensionality reduction, clustering, STLF with different algorithms from which the Best Performant Algorithm (BPA) is automatically selected to provide STLF for the next 24 hours. The methodology is ultimately tested considering a real case of a residential smart building.