Why Is Day-Ahead Price Forecasting So Hard? A Data-Driven Approach to Weather, Outages, Transmission Constraints and Demand Uncertainty

## The Complex Equation Behind PTF The Market Clearing Price (PTF) is not the result of a single variable, but of dozens of factors interacting simultaneously. Weather conditions, plant outages, transmission constraints, demand fluctuations, and even regulatory decisions shift the price hour by hour, sometimes minute by minute. This multi-dimensional structure makes PTF forecasting one of the most challenging data science problems in the energy sector. ## Weather: A Variable Affecting Both Demand and Supply Temperature directly affects heating/cooling demand. But it also determines the output of wind and solar plants. For hydropower, precipitation and snowmelt shape water flow and, in turn, production capacity. The fact that these three effects can pull the same price in opposite directions simultaneously makes the job of forecasting models considerably harder. ## Outages and Transmission Constraints: Unpredictable Shocks An unexpected plant outage creates a sudden contraction on the supply side. Similarly, capacity constraints on transmission lines can disrupt regional supply-demand balance, leading to price spikes. Because these events are by nature low-probability but high-impact, they represent tail-risk scenarios that classical statistical models struggle to capture. ## Demand Uncertainty: The Behavioral Dimension Demand forecasting doesn't depend on weather alone — the rhythm of industrial production, holidays, and even broader economic conditions all play a role. Some of these variables follow regular patterns, while others behave unpredictably. ## The Value of a Data-Driven Approach HYDROWISE's approach to PTF forecasting doesn't rely on a single "crystal ball" model, but on a structure that processes multiple data sources together — historical and forecast meteorological data, operational SCADA data, and EPİAŞ market data. This allows the relative impact of each variable on price to be disentangled, and the forecast's confidence interval to be expressed more realistically. ## The Practical Takeaway for HPP Operators While a perfect PTF forecast isn't possible, knowing how uncertain you are is valuable in itself for sizing positions and calibrating risk tolerance. This forms the basis for decisions about how aggressively to position in GÖP and how much reserve to hold back for GİP.