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EncartaLabs

Electric Load Forecasting - Fundamentals and Best Practices

( Duration: 2 Days )

The Electric Load Forecasting - Fundamentals and Best Practices training course introduces electric load forecasting from both statistical and practical aspects using language and examples from the power industry. Through conceptual and hands-on exercises, you will experience load forecasting for a variety of horizons from a few hours ahead to 30 years ahead. The overall aims are to prepare and sharpen the statistical and analytical skills in dealing with real-world load forecasting problems and improve your ability to design, develop, document, and report sound and defensible load forecasts.

By attending Electric Load Forecasting - Fundamentals and Best Practices workshop, delegates will learn to:

  • Classify load forecasts
  • Use basic graphic methods to discover the salient features of load profiles
  • Build a benchmark model for a wide range of utilities
  • Capture special effects for a local utility
  • Forecast loads for both small and large utilities
  • Improve very short-term forecasting accuracy
  • Perform weather normalization
  • Use macroeconomic indicators for long-term load forecasts
  • Detect outliers
  • Continue improving forecasting practice
  • Avoid making frequently made mistakes

  • Basic knowledge of the utility industry
  • BSasic understanding of forecasting

This Electric Load Forecasting - Fundamentals and Best Practices class is intended for Load/price forecasters, energy traders, quantitative/business analysts in the utility industry, power system planners, power system operators, load research analysts, and rate design analysts.

COURSE AGENDA

1

Introduction to Electric Load Forecasting

  • Overview of the electric power industry
  • Business needs of load forecasts
  • Driving factors of electricity consumption
  • Classification of load forecasts
  • Software applications
2

Salient Features of Electric Load Series

  • A general approach to electric load forecasting
  • Overview of the data pool
  • Trend and seasonality
  • More salient features
3

Multiple Linear Regression

  • Naive models
  • Trend
  • Class variables
  • Polynomial regression
  • Interaction regression
  • Rolling regression
4

A Naive Benchmark for Short-term Load Forecasting

  • Motivation
  • Criterion
  • A naive MLR benchmark
  • Applications
5

Customizing the Benchmarking Model

  • Recency effect
  • Weekend effect
  • Holiday effect
  • Case studies
6

Very Short-Term Load Forecasting

  • Hour ahead load forecasting
  • Weighted least squares regression
  • Dynamic regression
  • Two-stage method
  • Extensions
7

Medium/Long-Term Load Forecasting

  • Macroeconomic indicator
  • Weather normalization
  • Forecasting with weather variation
  • Forecasting with cross scenarios
8

Variables, Methods, Techniques, and Further Readings

  • Load, weather, calendar, macroeconomic indicator, etc.
  • Similar day and hierarchy
  • Regression
  • ARIMA
  • Exponential smoothing
  • Support vector machine
  • Artificial neural networks
  • Fuzzy systems and fuzzy regression
  • Relevant and readable books
  • Load forecasting papers
9

Frequently Made Mistakes

  • Counterexamples
  • Expectation
  • Data
  • Models
  • Decisions
10

Software Applications

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 6,000 various courses on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

View our other course offerings by visiting https://www.encartalabs.com/course-catalogue-all.php

Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.

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