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Efficient Training of Learning-Based Thermal Power Flow for 4th Generation District Heating Grids
Computing the TPF, i. e., determining the grid state consisting of temperatures, pressures, and mass flows for given supply and demand values, is classically done by solving the nonlinear heat grid equations, but can be sped up by orders of magnitude using learned models such as neural networks.
Bidding and Scheduling in Energy Markets: Which Probabilistic Forecast Do We Need?
For bidding curve optimization, pairwise or full joint distributions are necessary except for specific cases.
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