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Short-Term Load Forecasting 2019

Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these...

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Bibliographic Details
Main Author: Gabaldón, Antonio
Other Authors: Ruiz-Abellón, Dr. María Carmen, Fernández-Jiménez, Luis Alfredo
Format: eBook
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
Special Days
Dbn
Random Forest
Forecasting
Bus Load Forecasting
Short-term Load Forecasting
Data Augmentation
Feature Selection
Deep Learning
Electricity Consumption
Residential Load Forecasting
Regressive Models
Time Series
Wavenet
History Of Engineering & Technology / Bicssc
Seasonal Patterns
Multivariate Random Forests
Psr
Hybrid Energy System
Prosumers
Feature Extraction
Load Forecasting
Nordic Electricity Market
Performance Criteria
Cubic Splines
Weather Station Selection
Building Electric Energy Consumption Forecasting
Transfer Learning
Convolution Neural Network
Combined Model
Multiobjective Optimization Algorithm
Component Estimation Method
Load Metering
Electricity
Tikhonov Regularization
Day Ahead
Demand Response
Lasso
Data Preprocessing Technique
Multiple Sources
Modeling And Forecasting
Vstlf
Univariate And Multivariate Time Series Analysis
Deep Residual Neural Network
Electric Load Forecasting
Hierarchical Short-term Load Forecasting
Long Short-term Memory
Electricity Demand
Pattern Similarity
Cost Analysis
Preliminary Load
Power Systems
Real-time Electricity Load
Distributed Energy Resources
Cold-start Problem
Demand-side Management
Short Term Load Forecasting
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary:Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (324 p.)
ISBN:9783039434435
9783039434428
books978-3-03943-443-5

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