Power Transmission & Distribution > Advanced Load Forecasting & Methodology – Virtual Instructor Led Training (VILT)

About this Virtual Instructor Led Training (VILT)

This 5 half-day Virtual Instructor Led Training (VILT) course is focused on load forecasting in the power industry, a critical aspect of managing electricity generation and distribution with a focus on key areas described  as follows:

Load Forecasting: The course presents advanced load forecasting techniques, which are essential for predicting electricity demand accurately. Load forecasting is critical for grid management and resource planning across all forecasting time horizons.

Temporal Granularity: Some regions have transitioned to load forecasts with finer temporal granularity, moving beyond hourly predictions. This shift aims to reduce forecasting uncertainties and improve real-time grid management and generate improved hybrid methodologies

Frequency of Updates: As the operation of electric grids becomes more complex, there’s a need for more frequent updates in load forecasts, due to the increased penetration of inverter-based resources, which can vary in output rapidly.

Weather Sensitivity Analysis: The course examines how weather conditions impact load forecasts. It covers weather sensitivity analysis, which quantifies the influence of meteorological factors on electricity demand. It presents the weather impacts on the load forecasts and the methodologies employed to quantify the weather effect and building a repository of weather normal data.

Statistical and Mathematical Models: The course delves into the application of statistical and mathematical models for load forecasting. These models often include time series analysis, regression analysis, and other mathematical tools to make predictions.

Artificial Intelligence and Machine Learning (AI/ML): The course highlights the integration of AI and ML techniques in load forecasting. Machine learning algorithms are used for data-driven predictions and pattern recognition, improving forecast accuracy.

Grid Complexity: As the power grid becomes more complex due to increased penetration of renewable and inverter-based resources, load forecasts require higher temporal resolution and adaptability to changing conditions.

Practical Applications-Industry Examples: It emphasizes practical applications of forecasting methods, supported by real-life examples from large control areas in North America, Australia, Europe. This approach helps professionals understand how to apply these methods effectively.

This seminar has a Guest Speaker to provide insights into the most modern aspects of Artificial Intelligence.

Upon completion of this VILT course, the participants will be able to:

  • This course offers a comprehensive approach to all aspects of load forecasting.
  • It provides insights to both operators in the generating plant and also to system operators.
  • Provides a review of the advanced concepts and forecasting methodologies,
  • Artificial Neural Networks and Probabilistic forecasting methods to manage forecasting uncertainties in short time frames,
  • Market segmentation and Econometric framework for long term forecast,
  • Most recent practical examples from large power companies
  • In depth discussion of recent industry reports
  • Energy forecasting professionals
  • Energy Planners and Energy Outlook Forecasters and Plant Operators
  • Fuel procurement professionals
  • Planners and schedulers of thermal generating units
  • Energy professionals
  • Intermediate

The VILT course will be delivered online in 5 half-day sessions comprising 4 hours per day, including time for lectures, discussion, quizzes and short classroom exercises.

Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total).

Your first expert course leader is a Utility Executive with extensive global experience in power system operation and planning, energy markets, enterprise risk and regulatory oversight. She consults on energy markets integrating renewable resources from planning to operation. She led complex projects in operations and conducted long term planning studies to support planning and operational reliability standards. Specializing in Smart Grids, Operational flexibilities, Renewable generation, Reliability, Financial Engineering, Energy Markets and Power System Integration, she was recently engaged by the Inter-American Development Bank/MHI in Guyana. She was the Operations Expert in the regulatory assessment in Oman. She is a registered member of the Professional Engineers of Ontario, Canada. She is also a contributing member to the IEEE Standards Association, WG Blockchain P2418.5.

With over 25 years with Ontario Power Generation (Revenue $1.2 Billion CAD, I/S 16 GW), she served as Canadian representative in CIGRE, committee member in NSERC (Natural Sciences and Engineering Research Council of Canada), and Senior Member IEEE and Elsevier since the 90ties. Our key expert chaired international conferences, lectured on several continents, published a book on Reliability and Security of Nuclear Power Plants, contributed to IEEE and PMAPS and published in the Ontario Journal for Public Policy, Canada. She delivered seminars organized by the Power Engineering Society, IEEE plus seminars to power companies worldwide, including Oman, Thailand, Saudi Arabia, Malaysia, Indonesia, Portugal, South Africa, Japan, Romania, and Guyana.

Your second expert course leader is the co-founder and Director of Research at Xesto Inc. Xesto is a spatial computing AI startup based in Toronto, Canada and it has been voted as Toronto’s Best Tech Startup 2019 and was named one of the top 10 “Canadian AI Startups to Watch” as well as one of 6th International finalists for the VW Siemens Startup Challenge, resulting in a partnership. His latest app Xesto-Fit demonstrates how advanced AI and machine learning is applied to the e-commerce industry, as a result of which Xesto has been recently featured in TechCrunch.

He specializes in both applied and theoretical machine learning and has extensive experience in both industrial and academic research. He is specialized in Artificial Intelligence with multiple industrial applications. At Xesto, he leads projects that focus on applying cutting edge research at the intersection of spatial analysis, differential geometry, optimization of deep neural networks, and statistics to build scalable rigorous and real time performing systems that will change the way humans interact with technology. In addition, he is a Ph.D candidate in the Mathematics department at UofT, focusing on applied mathematics. His academic research interests are in applying advanced mathematical methods to the computational and statistical sciences. He earned a Bachelor’s and MSc in Mathematics, both at the University of Toronto.

Having presented at research seminars as well as instructing engineers on various levels, he has the ability to distill advanced theoretical concept to diverse audiences on all levels. In addition to research, our key expert is also an avid traveler and plays the violin.

To further optimise your learning experience from our courses, we also offer individualized “One to One” coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster.
Request for further information about post training coaching support and fees applicable for this.