Power Demand Daily Predictions Using the Combined Differential Polynomial Network

Abstract

Power demand prediction is important for the economically efficient operation and effective control of power systems and enables to plan the load of generating unit. A precise load forecasting is required to avoid high generation cost and the spinning reserve capacity. Under-prediction of the demands leads to an insufficient reserve capacity preparation and can threaten the system stability, on the other hand, over-prediction leads to an unnecessarily large reserve that leads to a high cost preparations. Cooperation on the electricity grid requires from all providers to foresee the load within a sufficient accuracy. Differential polynomial neural network is a new neural network type, which forms and resolves an unknown general partial differential equation of an approximation of a searched function, described by data observations. It generates convergent sum series of relative polynomial derivative terms, which can substitute for the ordinary differential equation, describing 1-parametric function time-series with partial derivatives. A new method of the short-term power demand prediction, based on similarity relations of subsequent day progress cycles is presented and tested using the combined differential polynomial network.