June 6-8 Utility Analytics 202: Introduction to Forecasting in the Utilities (Virtual)

Starts:  Jun 6, 2023 10:00 (CT)
Ends:  Jun 8, 2023 16:00 (CT)

This UAI Utility Analytics 202: Introduction to Forecasting in the Utilities takes place June 6-8, 2023 (Training class times will be provided soon) and will be held via Virtual Classroom format.

*Please note this class will take part over 5 hours each day.

Description:
Continued population growth, socioeconomic improvements, and technological advancements in the past few decades have caused a significant rise in the consumption of energy and materials. Many utilities find themselves concerned -- the volatility of wind and solar power generation, the uncertainty of rooftop solar adoption, and rising gas and electricity prices pose serious challenges. The modern consumer-centric paradigm of transactive energy has changed
the traditional load forecasting methodologies, as it evolves and reshapes utility strategies.

This training intends to provide a comprehensive introduction to forecasting methods and present enough information about each method for participants to use them sensibly. Examples and applications from the utility industry, including forecasting with AMI data, are ncluded.

Audience:
This training is intended for the following audiences:
1. Analytics professionals who are interested in learning forecasting methods with
applications in utilities.
2. Utility professionals who find themselves doing forecasting without prior formal
training.
3. Utility Analytics 101 completers who want to continue advancing their analytics
knowledge in the utility setting.
4. Positions include, but are not limited to, Data Scientists, Forecasting Analysts, Energy Analysts, and Research Analysts.

Prerequisites:
College- or university-level statistics and algebra or equivalent experience. Some exposure to statistical programming (for example, in Python or R language) is helpful but not required.

Objectives:
Upon completion of this training, students will be able to:
1. Understand select applications of time series forecasting within the utility sector.
2. Use statistical and graphical approaches to exploratory data analysis with time series data.
3. Use software and/or programming languages (e.g., Python or R) to create statistical forecasts.
4. Develop load, price, wind power, and/or solar power forecasts.

If you have any additional questions, please reach out to @Leslie Cook at lcook@utilityanalytics.com

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