Big Data Analytical Techniques for Electrical Energy Forecasting in Smart Grid Paradigm

Electrical energy demand forecasting is fundamental for stable operation of an electrical grid to maintain a continuous balance between supply from generating stations and consumers' demand. It facilitates optimum and economical utilization of resources. This fundamental problem is gaining atte...

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Bibliographic Details
Published inApplications of Big Data and Artificial Intelligence in Smart Energy Systems Vol. 1; pp. 101 - 126
Main Authors Rawal, Keerti, Ahmad, Aijaz
Format Book Chapter
LanguageEnglish
Published River Publishers 2023
Edition1
Subjects
General Engineering & Project Administration
General References
Software Engineering
RR
Electrical Demand Data
PR
DTR
Demand Forecasting
LPWAN
Ml Algorithm
DE Algorithm
Lasso Algorithm
Big data
ANFIS
High Variance Data
Peak Load Forecasting
feature selection
RFR
Supervise Ml Algorithm
smart grid
Wrapper Methods
Absolute Percentage Error
Supervise Ml
machine learning
Hybrid Renewable Energy System
Attribute Selection
Attribute Selection Method
Input Features
renewable energy forecasting
MedAE
electrical demand forecasting
Time Varying Electricity Pricing
Online AccessGet full text
ISBN9788770228251
9788770229944
8770228256
8770229945
DOI10.1201/9781003440710-5

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Table of Contents:
  • 5.1 Introduction 5.2 Attribute Selection Techniques 5.3 Statistical Characterization of Data 5.4 Machine Learning Techniques of Forecasting 5.5 Comparative Analysis of Machine Learning Techniques 5.6 Renewable Energy Forecasting 5.7 Conclusions and Discussion References