Why Have Multiple Global Energy Consumption Forecast Services?

See how to navigate the effects of weather events, changing conditions, and holidays on your power market needs – plus what to expect on less predictable days. 

Accurate forecasting models allow power providers to optimize generation capacity, manage fuel supplies, and integrate renewable energy more efficiently and effectively. Forecasting models also help power companies predict price movements, hedge against market volatility, and make smart purchasing decisions.

However, forecasting involves a significant amount of uncertainty. This uncertainty primarily stems from two sources:

• Weather forecasts: Weather is a critical input for energy consumption forecasts. Variables such as temperature and humidity directly impact demand predictions. However, weather forecasts are inherently uncertain, adding a layer of variability to the demand forecast.
• Demand load models: The accuracy of demand forecasts also depends on the models used. Different methods can yield varying results, and the choice of weather data further influences these outcomes. Some energy demand models perform better under specific conditions, such as extreme weather events.

Global Energy Forecast Models

While smaller companies typically rely on a single model due to cost constraints, larger companies often use several forecasting models to cover various aspects of their operations.

Having access to various forecasts helps in choosing the most accurate one for specific scenarios. That’s important because no single model is always right, and different models have different strengths and weaknesses. 

That’s why our proprietary TESLA energy forecasting services work with different vendors, integrating multiple weather forecasts into one model. For example, one vendor may be stronger when it’s rainy, while another tends to be more accurate during heat waves. 

Our linear regression model identifies and follows existing trends. This provides a clear understanding of how different variables impact demand. Another benefit of our model is that it allows us to explain our forecasts when customers have questions, something machine-learning models don’t allow for.  

Plus, our forecasting services offer a high level of control and visibility, especially around holidays and special events.

Global Energy Consumption Forecast High-Impact Days

Customers have shared that our energy demand models are particularly effective during certain high-impact days. These include: 

• Holidays and days around holidays: Our regression model allows us to isolate the effects of specific days (such as Independence Day or Christmas Eve) and adjust forecasts accordingly.
• Extreme weather shifts: Our models’ accuracy surpasses our competitors, especially during significant weather changes, like sudden temperature spikes or drops, which can drastically alter demand patterns.

Although weather forecasting models may have similar results for 300 days a year, the models can vary for the remaining 65 days. You may find that one vendor is more accurate for 20 of those days while another vendor surpasses the others for five days a year. 

However, those five days might be critical to your forecasting needs. And since the highest demand days will likely have higher prices, you’ll want to ensure you’re using the most accurate forecasting model for those pivotal days. 

Next, let’s look at two examples for how you might use our forecasting models. 

Weather Forecasts Example

Fluctuations in weather patterns can make electricity demand predictions challenging. And if you don’t account for weather in your past electricity demand analysis, you can be off base in future projections. 

In the example below, we see three load forecasts for the Electric Reliability Council of Texas (ERCOT). All were generated using the same model. The differences in the load forecasts are caused entirely by the differences in the weather forecast data run through the model.

Yes Energy TESLA forecasting services

For Friday and Saturday, there's a larger spread at the peak than for Sunday and Monday, where there seems to be a lot of agreement between the weather forecasts. Consensus around the weather forecast should give you confidence in the load forecast but might result in less opportunity for spreads between the day-ahead and real-time prices.

Regional Transmission Organization (RTO) Load/Demand Model Example 

The example below highlights our primary load forecast and the public forecast generated by the regional transmission organization (RTO). We can’t say for sure what forecasting method each RTO uses to generate their load forecast, but we can observe the weather patterns and calendar days when they perform better or worse relative to our load forecasting model.

Yes Energy TESLA forecasting services

This allows us to track which models perform best during peak temperature days in the summer. We can also view transitions between cold and hot days in the spring and fall or track holidays and special days around holidays. 

Sourcing load forecasts generated by different methodologies enables you to take advantage of models that perform better in different situations.

What Does This Mean for You? 

When you’re looking to understand how different variables impact demand, you want multiple forecasting services. By leveraging multiple forecasts, you can ensure you’re prepared for various scenarios, leading to better decision-making and resource management.

At Yes Energy®, our forecasting models stand out due to our ability to handle uncertainty, provide detailed insights around special days, and adapt to extreme weather conditions. 

For wholesale market forecasts, we archive our predictions for back-testing. You can then assess how profitable your trading strategies would have been using our forecasts. 

We also offer custom trials for larger organizations. Reach out to learn more or request a demo. 

About the Author: Ben Perry is the senior product manager of forecasting at Yes Energy. Ben brings 10 years of experience as a power demand forecast analyst at TESLA to the Yes Energy product team. He's now focused on applying that experience to steering the Yes Energy forecasting product roadmap to best serve the industry through the energy transition.