Regarding the complex behaviour of price signalling, its prediction is difficult, where an accurate forecasting can play an important role in electricity markets. In this paper, a feature selection based on mutual information is implemented for day ahead prediction of electricity prices, which are so valuable for determining the redundancy and relevancy of selected features. A combination of wavelet transform (WT) and a hybrid forecast method is presented based on a neural network (NN). Furthermore, an intelligent algorithm is considered for a prediction process to set the proposed forecast engine free parameters based NN. This optimisation process improved the accuracy of the proposed model. To demonstrate the validity of this model, the Pennsylvania-New Jersey-Maryland (PJM) electricity market is considered as a test case and compared with some of the most recent price forecast methods. These comparisons illustrate the effectiveness of the proposed strategy.
realized. A set of results show a good agreement between measurement and simulation. This antenna is suitable for a reflector antenna with an F/D ratio equals to 1.8, because the edge taper is near to À11 dB for the half subtended angle equal to 15 . The results of the reflector fed by the EBG dual band antenna show a maximum gain of about 44.5 dB for the lower band and 48.5 dB for the higher one.
Abstract:A novel compact band-notch ultra-wideband (UWB) printed monopole antenna is proposed, where the band notch characteristic is realized by inserting a pair of hook-shaped slit in the both side of radiating patch. Also, by inserting a π-shaped stub in the ground plane, additional resonance is excited and hence the bandwidth is increased up to 123%. This novel monopole antenna has ultrawide impedance bandwidth, compact size, low fabrication cost, and omnidirectional H-plane radiation patterns which are suitable for various broadband applications.
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