foreDAM: How AI Day-Ahead Market Forecasting Changes the Game for Discoms
- inspiri_admin
- Nov 11, 2020
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Day-ahead price and volume forecasting with machine learning gives buyers and sellers a genuine planning edge in the power exchange — here is how foreDAM turns 96 market blocks into a procurement strategy.
The Day-Ahead Market (DAM) on the power exchange clears electricity in ninety-six fifteen-minute blocks for every trading day. For a distribution company, the difference between a good month and a painful one often comes down to how well those ninety-six blocks were anticipated. Bid too conservatively and you over-procure through costly Bilateral or Deviation Settlement mechanisms; bid too aggressively and you are exposed to unfavourable clearing prices.
foreDAM is our AI-enabled price and volume forecasting engine for the Day-Ahead Market. It combines historical market clearing data with power system parameters — weather-driven demand, generation outages, renewable injection patterns and transmission constraints — to project the expected clearing price and volume for each block of the next trading day.
What the forecast actually tells you
Each morning, subscribers receive a block-wise projection covering the full ninety-six block horizon. The projection is not a single number but a band with confidence intervals, so schedulers can distinguish between blocks where prices are highly predictable and blocks where volatility — typically around the evening peak or during renewable ramps — warrants a more defensive procurement posture.
- Block-wise price bands for all 96 DAM sessions of the next trading day
- Expected volume traded, helping gauge liquidity before bidding
- Peak-window flags where deviation risk is highest
- Portfolio suggestions for mixing DAM, RTM and bilateral purchases
Why machine learning, and not classical curves
Classical approaches — merit-order stacking on nameplate capacities, or simple seasonal averages of historical clearing prices — degrade quickly when the generation mix shifts. A cloudy week in a solar-rich state or the commissioning of a new thermal unit moves the supply curve in ways that static models cannot see. Machine learning models trained on rolling windows of exchange data, weather forecasts and outage reports absorb these shifts continuously.
“Excellence is never an accident. It is always the result of high intentions, sincere efforts and intelligent Execution.”
We launched foreDAM on Good Friday, 2nd April 2021, after months of back-testing against real exchange outcomes. For Discoms and SLDCs the promise is straightforward: lower cost of power purchase through smarter bidding, better regulatory compliance, and a defensible, data-driven basis for every procurement decision.
