| Code | Date | Format | Currency | Team of 10 Per Person* |
Team of 7 Per Person* |
Early Bird Fee Per Person |
Normal Fee Per Person |
|---|---|---|---|---|---|---|---|
| PWR1522 | 21 - 25 Sep 2026 | Singapore | SGD | 6,191 | 6,479 | 6,999 | 7,199 |
| PWR1522 | 21 - 25 Sep 2026 | Singapore | USD | 4,815 | 5,039 | 5,399 | 5,599 |
*Fee per person in a team of 7 or 10 participating from the same organisation, registering 6 weeks before the course dateRequest for a quote if you have different team sizes, content customisation, alternative dates or course timing requirements Request for in-person classroom training or online (VILT) training format
Learn in teams and save more! Enjoy group discounts of up to 50% off normal fees for team based learning. Contact us on [email protected] to learn more today!
Code
PWR1522Date
21 - 25 Sep 2026Format
SingaporeCurrency
SGDTeam of 10
Per Person*
6,191
Team of 7
Per Person*
6,479
Early Bird Fee
Per Person
6,999
Normal Fee
Per Person
7,199
Code
PWR1522Date
21 - 25 Sep 2026Format
SingaporeCurrency
USDTeam of 10
Per Person*
4,815
Team of 7
Per Person*
5,039
Early Bird Fee
Per Person
5,399
Normal Fee
Per Person
5,599
*Fee per person in a team of 7 or 10 participating from the same organisation, registering 6 weeks before the course dateRequest for a quote if you have different team sizes, content customisation, alternative dates or course timing requirements Request for in-person classroom training or online (VILT) training format
About this Course
This 5 full-day 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 training course will also feature a guest speaker, who is a Ph.D candidate to provide insights into the most modern aspects of Artificial Intelligence in the context of load forecasting.
This course offers a comprehensive approach to all aspects of load forecasting:
- Gain a perspective of load forecasting from both operators in the generating plant and system operators.
- Understand and review the advanced load forecasting concepts and forecasting methodologies
- Learn the application of Artificial Neural Networks and Probabilistic Forecasting methods to manage forecasting uncertainties in short time frames
- Appreciate market segmentation and econometric framework for long term forecasts
- Find out the most recent practical application of load forecasting as examples from large power companies
- Get access to recent industry reports and developments
- Energy load forecasting professionals from power plant and system operators
- Energy planners and energy outlook forecasters and plant operators
- Fuel procurement professionals
- Planners and schedulers of thermal generating units
- Intermediate
Unlock the potential of your workforce with customized in-house training programs designed specifically for the energy sector. Our tailored, in-house courses not only enhance employee skills and engagement but also offer significant cost savings by eliminating travel expenses. Invest in your team’s success and achieve specific outcomes aligned with your organization’s goals through our expert training solutions. Request for further information regarding our on-site or in-house training opportunities.
In our ongoing commitment to sustainability and environmental responsibility, we will no longer providing hard copy training materials. Instead, all training content and resources will be delivered in digital format. Inspired by the oil and energy industry’s best practices, we are leveraging on digital technologies to reduce waste, lower our carbon emissions, ensuring our training content is always up-to-date and accessible. Click here to learn more.
Learn what past participants have said about EnergyEdge training courses
The training was very engaging, up to trend and on point. The trainer is very experienced.
Senior ERO, ERC
The people here at EnergyEdge are lovely and very helpful in supporting our professional learning.
Energy Regulation Officer I, ERC
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