Use of neural networks for customer tariff exploitation by means of short-term load forecasting
In the present paper, the possibility of using the neural networks to forecast the power absorbed by a user of electric energy, in order to exploit better the contractual interface with the utility, is investigated. The network chosen for this purpose is a radial basis function trained with leave-one-out cross validation. A prototype device, able to make forecasts using the proposed technique has been built and installed at the terminals of a test load. Several months of operation have shown that the implemented algorithm gives very good forecasts of the daily peaks of the load, thus allowing some load management to take place.
neural networks load forecasts power systems demand