Abstract
This paper presents four refined distance models to the application of forecasting short-term electricity price namely Euclidean norm, Manhattan distance, cosine coefficient, and Pearson correlation coefficient. The four refined models were constructed and used to select the days, which are like a reference day in electricity prices and loads, called similar days in this study. Using the similar days, the electricity prices of a forecast day were further obtained by similar day regression (SDR) and similar day based artificial neural network (SDANN). The simulation results of the case of the PJM (Pennsylvania, New Jersey and Maryland) interchange energy market indicate the superiority and availability of the selection 45 framework days and three similar days based on Pearson correlation coefficient model.
Highlights
Under liberalization of electricity industry, the bidding of electricity price is an important operation model of the market operation
Scholars usually used classical regression analysis theory [4,5,6] to forecast the price for managing location marginal price (LMP)
We refine four distance models to calculate the similarity between the similar days and reference day, which is the day before the forecasting day
Summary
Under liberalization of electricity industry, the bidding of electricity price is an important operation model of the market operation. The forecast of electricity price is very helpful for the bidding strategies of the market participants [1,2]. We can consider the historical price and system load as the factors which impact on price [3]. The historical data and proper training are non-linear, and mass data of load and electricity always decrease the level of forecasting accuracy. The electricity price forecasting was concerned by the combination of the electricity price and load data to increase the accuracy. The four models of choosing similar days, Euclidean norm, Manhattan distance, Cosine coefficient, and Pearson correlation coefficient were discussed to the influence of the forecasting accuracy of the electricity price
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