A novel evolutionary neural network ensemble model based on particle swarm optimization(PSO) is proposed to forecast market clearing price(MCP) in day-ahead electricity market.

In day-ahead electricity market,market clearing price(MCP)has a great effect on economic benefit of market participants including independent power producer,transmission and distribution service provider,retailer,and customer.

A novel neural network ensemble(NNE)model based on wavelet transformation and swarm intelligence evolution is proposed to forecast market clearing price(MCP)in day-ahead electricity market.

ν-support vector machine is employed to achieve system marginal price prediction and confidence interval estimation simultaneously by constructing and solving a convex optimization problem.

Fixed cost allocation based on system marginal price;

With the probability theory and probabilistic production simulation technologies, a probabilistic method for estimating marginal price in electricity markets with elastic demand is developed based on the concept of marginal genera.

This paper is about the strategies based on the marginal price prediction for independent power plants to connect to the national network by price competition in the context of electricity market.

Application of chaotic theory and fast BP ANN to forecast of marginal electricity price;