A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electricity grid. In essence, smart grids bring profound changes in the information systems that drive them: new information flows coming from the electricity grid, new players such as decentralized producers of renewable energies, new uses such as electric vehicles and connected houses and new communicating equipments such as smart meters, sensors and remote control points. All this will cause a deluge of data that the energy companies will have to face. Big Data technologies offers suitable solutions for utilities, but the decision about which Big Data technology to use is critical. In this paper, we provide an overview of data management for smart grids, summarise the added value of Big Data technologies for this kind of data, and discuss the technical requirements, the tools and the main steps to implement Big Data solutions in the smart grid context.

This chapter delves into the realm of “Big Data and Analytics in Smart Grid”, focusing specifically on the domain of forecasting energy consumption in educational institution buildings. The chapter starts with a high point of smart grid and forecasting electrical energy consumption in several areas and then describes the forecasting models in buildings for educational institutions. The study gives an overview of forecasting models in this kind of prediction, and it gives their potential and classification based on extensive studies and research. The chapter unfolds the practical advantages and challenges that big data offers to optimize energy forecasting for educational institutions. The exploration covers the entire big data pipeline in smart grid, including data selection, preparation, and the crucial phases of training and testing.

Recently, predictive analytic contributes very well for reliable electric power supply. It provides advanced techniques to process, interpret and analyze big energy data and make it more valuable. In this paper, we have presented a benchmark of the most used forecasting models in predicting electrical energy consumption for educational institutions. This study is based on a real use case, implemented using Big Data eco-system based on SMACK architecture. The proposed system analyzes six years of data sets that highly impact National School of Applied Sciences of El Jadida-Morocco energy consumption including planning data (courses, activities, holiday etc) and meteorological data (temperature, pressure, humidity etc). The aim of this benchmark is to evaluate the prediction performance of each forecasting model in order to choose the accurate one to predict electricity consumption in educational institutions.

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