Best Practices in Electricity Load Modeling and Forecasting for Long-Term Power System Planning

Ella Zhou; Sika Gadzanku; Cabell Hodge; Mike Campton; Stephane de la Rue du Can; Jingjing Zhang

doi:10.2172/1972011

Best Practices in Electricity Load Modeling and Forecasting for Long-Term Power System Planning

Ella Zhou
, Sika Gadzanku
, Cabell Hodge
, Mike Campton
, Stephane de la Rue du Can
, Jingjing Zhang

Lawrence Berkeley National Laboratory

Research output: NLR › Technical Report

Abstract

This document highlights the best practices on data acquisition and management, modeling and stakeholder engagement required for enhanced load modeling and forecasting. Each section is interspersed with case studies to highlight lessons from different country contexts to highlight both cross-cutting and location specific best practices needed to conduct robust load modeling and forecasting.

Original language	American English
Number of pages	35
DOIs	https://doi.org/10.2172/1972011
State	Published - 2023

NLR Publication Number

NREL/TP-7A40-81897

Keywords

best practices
data acquisition
load forecasting
load modeling
Philippines

Access to Document

10.2172/1972011

Cite this

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abstract = "This document highlights the best practices on data acquisition and management, modeling and stakeholder engagement required for enhanced load modeling and forecasting. Each section is interspersed with case studies to highlight lessons from different country contexts to highlight both cross-cutting and location specific best practices needed to conduct robust load modeling and forecasting.",

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AU - de la Rue du Can, Stephane

AU - Zhang, Jingjing

PY - 2023

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N2 - This document highlights the best practices on data acquisition and management, modeling and stakeholder engagement required for enhanced load modeling and forecasting. Each section is interspersed with case studies to highlight lessons from different country contexts to highlight both cross-cutting and location specific best practices needed to conduct robust load modeling and forecasting.

AB - This document highlights the best practices on data acquisition and management, modeling and stakeholder engagement required for enhanced load modeling and forecasting. Each section is interspersed with case studies to highlight lessons from different country contexts to highlight both cross-cutting and location specific best practices needed to conduct robust load modeling and forecasting.

KW - best practices

KW - data acquisition

KW - load forecasting

KW - load modeling

KW - Philippines

U2 - 10.2172/1972011

DO - 10.2172/1972011

M3 - Technical Report

ER -

Best Practices in Electricity Load Modeling and Forecasting for Long-Term Power System Planning

Abstract

NLR Publication Number

Keywords

Access to Document

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Cite this