Basma Saad

Artificial intelligence (AI) and its application across various domains have sparked significant interest, with each domain presenting distinct characteristics and challenges. In the renewable energies sector, accurate prediction of power output from photovoltaic (PV) panels using AI is crucial for meeting energy demand and facilitating energy management and storage. The field of data analysis has grown rapidly in recent years, with predictive models becoming increasingly popular for forecasting and prediction tasks. However, the accuracy and reliability of these models depend heavily on the quality of data, data preprocessing, model learning and evaluation. In this context, this paper aims to provide an in-depth review of previous research and recent progress in PV solar power forecasting and prediction by identifying and analyzing the most impacting factors. The findings of the literature review are then used to implement a benchmark for PV power prediction using deep learning models in different climates and PV panels. The aim of implementing this benchmark is to gain insights into the challenges and opportunities of PV power prediction and to improve the accuracy, reliability and explainability of predictive models in the future.

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.