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. Firstly， MCP series is decomposed into low-frequency and high-frequency parts by wavelet transformation， and learning samples for NNE input are constructed. Then， MCP forecasting problem is converted into error minimization problem between actual output and desired output， and extensive and intensive learning algorithms are used in two stages. In the first stage， construction and weights of neural network are designed to be particles in problem space， neural network are extensively trained by particle velocity and position update equations of particle swarm optimization， and NNE units with different constructions and initial weights are constructed. In the second stage， weights in units are intensively trained by gradient learning algorithm and cross validation， and the unit output with minimal error is regarded as the output of NNE. The novel model is applied to MCP forecasting of California day-ahead electricity market. Results show that the model can achieve satisfactory forecasting accuracy and is superior to BP neural network and ARIMA forecasting model.