Short-term price forecasting of Nordic power market by combination Levenberg–Marquardt and Cuckoo search algorithms

摘要

This study proposes a new forecasting method for short-term spot prices in the Nordic power market. It proposes a Cuckoo search Levenberg-Marquardt (CSLM)-trained, CSLM feed-forward neural network (CSLM-FFNN) for the solving process that combines the improved Levenberg Marquardt and Cuckoo search algorithms. The proposed model considers actual power generation and system load as input sets to facilitate the efficient use of both transmission and power generation resources by direct market participants. During the training, the proposed CSLM-FFNN model generalises the relationship between the area prices and the system price for the same period. The model can be updated to track online the variation trend of the electricity price and to maintain accuracy because of the rapid training speed in CSLM learning algorithm. The developed model is tested with publicly available data acquired from the Nord Pool, and the model's performance is compared with state-of-the-art artificial neural networks and time-series models. Besides, the proposed approach is applied to forecast market-clearing price in the Spanish electricity market, to further assess the validity of the approach. The results show that the proposed CSLM-FFNN exhibits superior performance than other methods in terms of forecasting accuracy and training efficiency.