Lucarella, D., Venturini, A., Canazza, V., Li, G. and Liu, C.C. (2005) An Intelligent System for Price Forecasting Accuracy Assessment. Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, Arlington, 6-10 November 2005, 92-99.

has been cited by the following article:

• TITLE: A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets

  AUTHORS: Nitin Singh, S. R. Mohanty

  KEYWORDS: Electricity Price Forecasting, Time Series Models, ARIMA, GARCH, ANN, Fuzzy ARTMAP

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.3 No.9, September 10, 2015

  ABSTRACT: In deregulated electricity markets, price forecasting is gaining importance between various market players in the power in order to adjust their bids in the day-ahead electricity markets and maximize their profits. Electricity price is volatile but non random in nature making it possible to identify the patterns based on the historical data and forecast. An accurate price forecasting method is an important factor for the market players as it enables them to decide their bidding strategy to maximize profits. Various models have been developed over a period of time which can be broadly classified into two types of models that are mainly used for Electricity Price forecasting are: 1) Time series models; and 2) Simulation based models; time series models are widely used among the two, for day ahead forecasting. The presented work summarizes the influencing factors that affect the price behavior and various established forecasting models based on time series analysis, such as Linear regression based models, nonlinear heuristics based models and other simulation based models.