Abstract
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.
Highlights
Future electricity grids consist in heterogeneous interconnected systems with an increasing number of small-scale generators and consumption appliances, providing large amounts of data
As proven in literature, there is no single method that exhaustively fulfil the requirements in terms of Short-Term Load Forecast (STLF) since target areas vary in size, in combination of commercial, residential and industrial consumers, in geographic, The associate editor coordinating the review of this manuscript and approving it for publication was Mostafa Rahimi Azghadi
Smart meters were installed during 2014, so that some records are missing or inconsistent; we consider for training and validation the 2015 data set, keeping the 2016 data set for incremental validation and testing of the Machine Learning (ML) algorithms
Summary
Future electricity grids consist in heterogeneous interconnected systems with an increasing number of small-scale generators and consumption appliances, providing large amounts of data. The electricity sector necessitates Big Data solutions and architectures for a performant energy system management. As proven in literature, there is no single method that exhaustively fulfil the requirements in terms of STLF since target areas vary in size, in combination of commercial, residential and industrial consumers, in geographic, The associate editor coordinating the review of this manuscript and approving it for publication was Mostafa Rahimi Azghadi. A new challenge is arising considering the large amount of available input data that continuously flows from smart meters and other sensors. For prosumers, consumption in correlation with volatility of distribution generation (wind turbines, photovoltaic panels, etc.) leads to new challenges
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
