Volume 8, Issue 2, April 2015, Pages 297 - 306
Short-term Power Demand Forecasting using the Differential Polynomial Neural Network
Received 7 January 2014, Accepted 15 October 2014, Available Online 1 April 2015.
- https://doi.org/10.1080/18756891.2015.1001952How to use a DOI?
- power demand prediction, week and day load cycle, differential polynomial neural network, sum relative derivative term, ordinary differential equation composition
- Power demand forecasting is important for economically efficient operation and effective control of power systems and enables to plan the load of generating unit. The purpose of the short-term electricity demand forecasting is to forecast in advance the system load, represented by the sum of all consumers load at the same time. 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. 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. A new method of the short-term power demand forecasting, based on similarity relations of several subsequent day progress cycles at the same time points is presented and tested on 2 datasets. Comparisons were done with the artificial neural network using the same prediction method.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Ladislav Zjavka PY - 2015 DA - 2015/04/01 TI - Short-term Power Demand Forecasting using the Differential Polynomial Neural Network JO - International Journal of Computational Intelligence Systems SP - 297 EP - 306 VL - 8 IS - 2 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2015.1001952 DO - https://doi.org/10.1080/18756891.2015.1001952 ID - Zjavka2015 ER -