24Business

In order for accurate predictions to save your energy company


Energy forecasts for net demand are crucial for participants of competitive markets, such as the Texas Electric Reliability Council (ERCOT) and similar markets, for several key reasons. For example, precise prognosis helps to predict when the imbalance of supply and demand will create jumps or demolition of prices, allowing traders and generators to optimize their license strategies. It is also important for the optimization of property. Electricity generators need to know when resources to market and price levels need to be committed. Poor prediction can lead to missed profit or operational assets when prices do not cover costs. Ercot region, specifically, has a huge wind and solar capacity. Net demand forecasts (total demand minus renewable generation) help predict when a conventional generation will be required to fill in the gaps from changing renewable resources. Market participants also use forecasts as a risk management tool. The exact projections allow participants to protect their positions through bilateral contracts or financial instruments, protecting against unstable market conditions. In the meantime, forecasts can provide an insight for operating planning. Having market predictions up to 15 days can help managers with the decisions on the obligations of the unit, the maintenance schedule and the distribution of resources in the generation property portfolio. In Texas, a competitive market design only for energy puts even more importance on prediction, as there is no payment of capacity-genedators generate income exclusively when they produce energy. The isolated network of states, extreme weather events and high renewable penetrations make a precise forecast and more challenging and financially due than in many other markets. Fortunately, artificial intelligence (AI) is now able to produce very accurate forecasts from the growing amount of meters and time data available. Complex and robust budgets derived from these machine learning algorithms are significantly more than what human analysts are capable of, making prediction of the system key to utilities. In addition, they are increasingly valuable for independent electricity manufacturers (IPPS) and other energy traders who make decisions about their positions in wholesale markets. Sean Kelly, co -founder and executive director SmartA company that provides solutions for a AI forecast, said the use of the Excel budget table as a prediction tool in order in 2005 when it started with work as an electricity trader, but this type of system is no longer appropriate today. “Now, we literally run in an ampere of four to six models behind the scenes, with five different suppliers of the weather that runs the ensemble each time,” Kelly said as a guest on Podcast of power. “So, as everything becomes confusing, we have to stay at the top and this is actually driven by machine learning.”



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