Why Is PTF Forecasting So Hard? Weather, Outages, Grid Constraints, and Demand Uncertainty — A Data-Driven View for Hydropower Operators

Hook + problem statement

Same reservoir, same turbines, same installed capacity yet tomorrow’s revenue can look wildly different depending on where the PTF lands hour by hour.
On paper, the task sounds straightforward: “Forecast the 24 hourly day-ahead prices.”
In reality, PTF is not just a continuation of yesterday’s curve. It is the market’s answer to a constrained clearing problem shaped by weather-driven load swings,
renewable variability, outages, and transmission constraints.
And because PTF comes out of an optimization-based matching of bids and constraints, it behaves less like a smooth time series and more like a system that can switch
regimes when conditions change [1][5]. That’s why PTF forecasting is not only about choosing an algorithm it’s about building the right data foundation,
designing features around what is known at bid time, and closing the loop with operational reality [2][6].

What exactly are we forecasting?

In Turkey’s Day-Ahead Market, PTF (Piyasa Takas Fiyatı) is the hourly clearing price derived from the matching of supply and demand bids under market and system constraints [1].
Forecasting PTF means forecasting where that clearing point will sit tomorrow for each hour, given what participants are likely to bid and what the system can realistically deliver.

Why electricity prices behave differently

Electricity is hard to store at scale, the balance is instantaneous, and demand can be inelastic in the short run.
This combination produces volatility, nonlinearity, and spikes, which the academic literature consistently flags as central challenges for price forecasting [5][6].
In other words: average accuracy matters, but tail events often dominate financial risk [6].

The “big four” drivers behind forecast difficulty

For our topic, we focus on four practical drivers that repeatedly show up in both research and market operations:

• Demand uncertainty: load forecast errors shift the marginal unit and can amplify volatility [5][10].
• Weather: affects load and renewables simultaneously; hydro is influenced through meteorological and hydrological pathways [5][7].
• Outages: sudden unavailability alters the supply curve abruptly—spike territory [6].
• Transmission constraints: congestion and system limits can force non-economic dispatch patterns and regime changes [9][10].

How it works

Variables: the driver families that matter

Across the literature, the most actionable variable groups are consistently framed as follows [5][6]:

• Price history & calendar effects: hour-of-day, weekday/weekend, holidays, seasonal patterns.
• Load and load uncertainty: expected load plus uncertainty proxies (forecast error bands) [5].
• Supply stack and generation mix: gas, coal, hydro, wind, solar levels; marginal fuel indicators [7][8].
• Fuel and macro proxies: gas prices, FX effects, broader cost drivers depending on context [8].
• Grid/system indicators: congestion signals, constraint regimes, planned interventions [10].
• Unit availability: planned maintenance and forced outages [6].

Data sources: where the inputs come from

A practical Turkey-focused pipeline typically combines:

• EPİAŞ Transparency Platform: market and system data visibility [2].
• EPİAŞ Transparency technical docs: how to access and integrate the services reliably [3].
• Weather forecasts: temperature, wind speed, solar irradiance, precipitation—ideally regionally granular.
• Grid signals (ENTSO-E / national sources): constraint signals, planned works, congestion hints [10].
• Plant SCADA/EMS: for HPPs—flow, head, unit status, kW output, gate positions (crucial for “quantity risk”).

Feature engineering: the “known-at-bid-time” rule

Most production failures in PTF forecasting are not caused by the choice of model—but by feature design.

Rule 1: No leakage ever.

If a feature includes information that is only known after delivery (e.g., realized load or realized generation),
the model will look great in backtests and fail in live operation.

Rule 2: Align features to the forecast horizon.

For day-ahead, use forecasts and plans available before gate closure (weather forecasts, load forecasts, planned maintenance).
Realized variables are fine for training and diagnostics, but you must replace them with bid-time proxies in production [5][6].

What it means for hydropower plants

Why PTF forecasting is more operational for HPPs

For thermal generation, price forecasting often revolves around fuel costs and market fundamentals.
For hydropower, there is an extra layer: you can shift water across hours. That makes price forecasting directly actionable—but also more sensitive to operational reality.

For HPPs, value comes from combining:

• Price insight (where PTF might go), and
• Production confidence (can we actually deliver in those hours?)

That second layer depends on flow uncertainty, unit availability, environmental constraints, and plant condition so PTF forecasting must be coupled with operational risk visibility.

Where forecast errors turn into money

• Moving water into the wrong hours (missed peaks)
• Getting the price right but the quantity wrong (availability/flow shifts)
• Last-minute reactive adjustments (decision quality drops)
• Risk appetite tightens as uncertainty grows (commercial pressure increases)

Example scenario / mini flow

Scenario: “Cold evening + wind uncertainty + constraint risk”

Assume tomorrow you see:

• Cold front risk in the evening → higher demand
• Wind forecast with wide error bands
• Planned grid work increasing constraint probability
• A turbine health signal moving closer to a warning threshold

Goal: Manage 17:00–22:00 with a balanced revenue-risk plan.

Decision flow

• Build demand scenarios: normal / cold / very cold
• Represent wind not as a single number but as an uncertainty range
• Treat grid constraints as a regime switch (constraint/no constraint) [10]
• Split HPP production into “secure” vs “optional” bands (SCADA-based)
• Output: hourly PTF distribution + action bands (not a single point)

Step-by-step flow (decision logic)

1. Build demand + weather scenarios
   Normal / cold / very cold
   For each scenario, define an hourly demand deviation band
2. Bring renewable uncertainty into the model
   Don’t treat wind/solar output as a single number—use a forecast range (P10–P50–P90)
   That range changes the shape (slope) of the supply curve, which shifts the PTF distribution [5][6]
3. Treat transmission constraints as a “regime”
   No constraint / constraint active (regional bottleneck)
   If constraints bind, the marginal unit can change [10]
4. Add HPP production risk (SCADA + maintenance signals)
   If Unit 2 shows elevated risk, don’t fully rely on it during peak hours
   Split the plan into a “secure generation” band and an “optional generation” band
5. Output: hourly PTF distribution + action band
   Instead of “increase water in these hours,” use:
   “increase water in these hours—but if the risk triggers, here’s the fallback plan.”

Mini note: This is not financial advice; the goal is to make the decision logic concrete.

Infographic Draft

DRIVER LAYER (4 Core Forces)

DATA LAYER (Sources)

FEATURE LAYER (Families)

Marginal Fuel Proxy: Cost indicators for natural gas and coal.

Constraint Flag: Transmission regime and supply-constraint markers.

Infographic – 1
An end-to-end blueprint that makes PTF forecasting systematic drivers → data sources → feature families → scenario/distribution outputs → an HPP decision band.
2024 Türkiye market scale and generation-mix indicators are included for context.

Hydrowise / Renewasoft approach

How the problem shows up in real operations

In many organizations, PTF forecasting lives in a separate spreadsheet while SCADA lives elsewhere. The result is predictable:

• Trading thinks it “called the price”
• Operations points to plant realities
• Finance sees the mismatch at day-end

A usable solution turns forecasting into a shared decision workflow.

Where Hydrowise fits

Hydrowise should be viewed as a modular, data-driven decision-support approach for hydropower operators—not as a replacement for EPİAŞ market systems or a standalone trading platform.

Its purpose is to help teams structure a shared workflow around forecasting + uncertainty + operational deliverability, so that trading, operations, and finance can reason from the same assumptions.

Conclusion + CTA

PTF forecasting is hard because it’s not merely “tomorrow’s price” it’s tomorrow’s market-clearing equilibrium under changing conditions:
weather-driven demand shifts, renewable uncertainty, outages, and grid constraints [1][5][6][10].
The practical way forward is a data-driven pipeline that respects bid-time information, avoids leakage,
produces scenario-aware outputs, and connects price insights to HPP operational risk.