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Improving Balance Responsibility with Better Daily Demand Forecasting

Everyone knows the feeling—a perfect entrée that has been spoiled by either too little salt or too much.... - Machine learning predictive analytics with no-code
19 July, 2023
Est. Reading: 3 minutes
IC İçtaş Energy needed to get daily electricity predictions into EPİAŞ and be ready to match supply and demand for the next day’s sell orders.
IC İçtaş Energy chose ML Studio for their daily electric consumption predictions.
Before using ML Studio for daily electric consumption predictions, the error rate was as high as 14–15%. Now, the error rate of IC İçtaş Energy has dropped to as low as 3%.

Everyone knows the feeling—a perfect entrée that has been spoiled by either too little salt or too much. Precision matters.

Since storing electricity in large quantities is not feasible, the total electricity production should be equal to consumption at any time. Too little production also causes heavy and unsustainable reliance on hydro, gas-powered turbines, nuclear energy, or other sources.

In Turkey, Energy Markets Operations Inc. (EPİAŞ), a Turkish energy exchange, works for the transparent, predictable, and sustainable operation and development of energy markets. EPİAŞ is also in charge of settling continuous energy imbalances in the electricity market.

If an energy company generates too much electricity—the surplus must be sold at a substantial loss. If it produces too little electricity—it must pay fines to EPİAŞ.

As the global economies continue to grow and the world becomes more urbanized—power demand forecasting becomes even more critical in the electric sector as it’s the foundation of the power system operation and control.

Reducing the Daily Forecasting Error Rate from 14-15% to Just 3%

IC İçtaş Energy, one of the leading Turkish energy companies since 1998, needs to have their daily electricity predictions into EPİAŞ by 11 a.m., make an order, and be ready to match supply and demand for the next day’s sell orders. Hence, the accuracy of their electricity demand predictions is absolutely mission critical to the business.

When determining the best practices to make their daily electricity predictions, modern energy suppliers understand that:

  • Manual predictions struggle to capture the comprehensive effects influencing the target—such as trends, weather, holidays, and schools closings.
  • Statistical methods have shown weaknesses in predicting and capturing the non-linear behavior of energy consumption data long-term
  • The computational approach has limited predictive capacity due to its non-stationary trend and the sharp patterns in energy consumption.

As a result, energy companies are increasingly turning to machine-learning solutions to improve the predictive quality of their daily forecasts. IC İçtaş Energy chose ML Studio. The result?

Before using ML Studio for daily electric consumption predictions, they had an error rate as high as 14–15 percent. Now, the error rate has plummeted to as low as three percent.

Generating accurate, short-term forecasts is essential for the energy industry. Accurate demand forecasting requires:

  • Accurate Data: No matter how much technology evolves—still nothing beats having the right data—especially precise weather forecasts.
  • The Right Features: It’s vital to understand historical data, such as last week’s or last month’s electricity consumption and surges and lulls for seasonality, nights and weekends, holidays, and the weather.
  • Constant Iteration: The environment we live in is constantly changing—people move and trends change. Predictions need to be constantly updated and models must be constantly monitored to better capture the current reality.

Energy production is a dynamic field. With the power of machine learning, İçtaş Energy can deliver the right amount of electricity to customers, save costs and satisfy investors, and cut down on CO2 emissions.

All electricity generation technologies emit greenhouse gasses at one point in their life-cycle. The more accurate the electricity predictions are each day—the less excess energy is generated—and the need to supply the energy deficiency by coal and gas diminishes.

What is the Carbon Intensity of Power?

Carbon intensity is a measure of CO2 equivalent emissions caused for each unit of electricity (kWh) generated in a country. The lower it is, the cleaner the power generated in that country.

Would you like to see how ML Studio can help your organization solve real-world problems with a robust and ready-to-use End-to-End AI platform that doesn’t go off budget? 

Get in touch for a personal demo.

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