Webinar: Smarter Load Forecasting: How Ensemble Methods Give You a Real Market Edge

In today’s volatile markets, relying on a single load forecast is a liability. Traditional single-model approaches often miss the mark precisely when accuracy matters most — during periods of fluctuating weather and high-stakes trading decisions. 

Join PCI experts from 1-2 p.m. CT on June 25 for a live demonstration of PCI Forecaster and see how ensemble modeling transforms load forecasting into a competitive advantage for portfolio optimization and trade scheduling. 

Here’s what you’ll learn: 

• What ensemble modeling is and why it outperforms single-model forecasting in volatile conditions 
• How to use AI/ML to build more accurate load forecasts 
• How to benchmark forecast performance across multiple models 
• How to ingest, compare, and synthesize multiple data streams simultaneously 
• The end-to-end process for producing highly accurate load forecasts in PCI Forecaster 

Who will benefit from this webinar: 

• Traders and scheduling coordinators who rely on load forecasts for unit commitment and trade decisions 
• Load forecasters and analysts looking to improve model accuracy and benchmarking practices 
• Portfolio managers and operations staff responsible for forecast-driven optimization 

Registrations are manually reviewed and approved, and you will receive confirmation usually within one business day. Registrants must register using email addresses issued by their employers. 

Registration closes at 12:55 p.m. Thursday, June 25 — 5 minutes before the start of the webinar. 

About the presenters:  

Dwarak Varaharajan: Dwaraknaath Varadharajan is PCI’s Sr. Product Manager, Optimization & AI. Dwaraknaath brings over more than five years of expertise in the energy industry and software development, having been an integral member of PCI since May 2019. Currently serving as the Product Manager for the BatteryTrader and Forecaster products, Dwaraknaath leads initiatives to enhance our capabilities and drive strategic development of products. Dwaraknaath’s skillset encompasses proficiency in multiple programming languages, generative AI, machine learning, business intelligence, and cloud software development. Dwaraknaath holds a master of science in electrical engineering with a specialized focus on power systems and minor focus on statistics from North Carolina State University. 

Khai Le: Khai Le is a senior vice president at PCI Energy Solutions with 50 years of experience working with market participants in RTO markets to deploy the PCI Enterprise Platform to automate their bidding, scheduling, settlement, and reporting workflows. Khai has conducted over 900 seminars on market-based operations, bidding strategies, portfolio optimization, and shadow settlements for utilities and RTOs worldwide. Khai received his bachelor’s degree from Harvey Mudd College and his master’s degree from Carnegie Mellon University. 

Arturo Custodio: Arturo Custodio Díaz is a Product Owner, Developer, and Software Analyst at PCI Energy Solutions, where he leads quarterly planning and product development for the Forecaster product. Arturo holds a professional degree in Mechatronics Engineering from the Pontifical Catholic University of Peru (PUCP). His background spans machine learning, predictive modeling, and data engineering, with hands-on experience in Python, SQL, TensorFlow, and cloud-based model deployment via Amazon SageMaker and AWS.