Abstract
Precise prediction of short-term electric load demand is the key for developing power market strategies. Due to the dynamic environment of short-term load forecasting, probabilistic forecasting has become the center of attention for its ability of representing uncertainty. In this paper, an integration scheme mainly composed of correlation analysis and improved weighted extreme learning machine is proposed for probabilistic load forecasting. In this scheme, a novel cooperation of wavelet packet transform and correlation analysis is developed to deal with the data noise. Meanwhile, an improved weighted extreme learning machine with a new switch algorithm is provided to effectively obtain stable forecasting results. The probabilistic forecasting task is then accomplished by generating the confidence intervals with the Gaussian process. The proposed integration scheme, tested by actual data from Global Energy Forecasting Competition, is proved to have a better performance in graphic and numerical results than the other available methods.
Highlights
IntroductionAs a fundamental business problem, load forecasting (LF) is associated with decision-making processes in power system plannings, operations, energy trading and so forth [2,3]
Electric load forecasting (LF) is an indispensable task for electric utilities in the long term [1].As a fundamental business problem, LF is associated with decision-making processes in power system plannings, operations, energy trading and so forth [2,3]
Additional input features of temperature data and other exogenous variables are helpful to short-term load forecasting (STLF) in some cases, the redundant part of these features will add into uncertainties for probabilistic load forecasting (PLF) [23,28,40]
Summary
As a fundamental business problem, LF is associated with decision-making processes in power system plannings, operations, energy trading and so forth [2,3]. Prediction of future load demand is a tendency forecasting subject based on actual load data. Methods of tendency forecasting are generally classified into three categories: the physical approaches, the statistical approaches and the artificial intelligence (AI) approaches [4]. The diversity of data sources makes it impossible to build physical models for LF [5]. Since the load demand is affected by the chaotic nature of weather conditions, statistical approaches such as exponential smoothing, auto regression (AR), moving average (MA) and their variants are insufficient to address the nonlinearity and randomness properties of load data [6]. AI approaches have become the mainstream of LF methods
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