The price prediction for the energy market based on a new method

Abstract

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.

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

“…Multitude researches have investigated the microgrid operation with various [4][5][6] distributed generations (DGs) Optimized coordinated power dispatch approach for a microgrid (MG) scheduling 7 Optimal method with high robustness for MG 8 Stochastic planning scheme for 24 h scheduling for an MG 9 Optimization method for reduction of whole expenditure in an MG 8 Multiobjective optimization methods by assuming expenditure, emission, etc 10,11 Multiobjective optimization for economic dispatch for microgrid with reliability 12 Management method for renewable sources 13 Hybrid-integer nonlinear approach for MG scheduling for achieving upper photovoltaic power 14 Smart power management of microgrid using artificial intelligence methods 15 Cost-effective combined heat and power dispatch with heat/energy reliance features [16][17][18][19] Demand response (DR) problem that can optimize the scheduling using optimization algorithm 20 DR program on stochastic power provided to big users by green resources 21,22 Other models [23][24][25] The considered MO optimization is handled in this work using an epsilon limitation approach. A fuzzy satisfying method is utilized for opting the best conciliation answer, and DRP is exerted to decrease the operation expenditure.…”

“…As shown in the work of Motevasel et al, the combined heat and power (CHP) units can be regarded as a heat/energy resource in an MG. The thermal power expenditure can be decreased by the utilization of loss heat in CHPs . An alternative is proposed by Alipour et al and Hamian et al for cost‐effective CHP dispatch with heat/energy reliance features.…”

“…The thermal power expenditure can be decreased by the utilization of loss heat in CHPs. 17 An alternative is proposed by Alipour et al 18 and Hamian et al 19 for cost-effective CHP dispatch with heat/energy reliance features.…”

Improved owl search algorithm for optimal capacity determination of the gas engine in aCCHPsystem using4Eanalysis

“…8,9 The system was validated by experimental data. 10 Zhang et al 11 developed a different configuration of a CCHP plant. To do so, the exhaust-gas-and-hot-water-driven absorption chiller (AC) was compared with three cooling modes, including AC combined with an electric chiller (EC), AC combined with a ground source heat pump (GSHP), and AC combined with gas-fired absorption chiller (GFC).…”

Computer-aided breast cancer diagnosis based on image segmentation and interval analysis

“…Several methods are performed on considering uncertainties, for example, fuzzy methods [22][23][24][25][26], statistical methods [27] and interval methods [28].…”