A comprehensive thermal load forecasting analysis based on machine learning algorithms
Precise forecasting of thermal loads is a critical factor for economic and efficient operation of district heating and cooling networks.If thermal loads are known with high accuracy in advance, use of renewable energies can be maximized, and – in combination with thermal storage units – fossil generation, in particular in peaking units, can be