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The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed...
Point forecasts suffer from unreliable and uninformative problems when the uncertainty level increases in data. Prediction intervals (PIs) have been proposed in the literature to quantify uncertainties associated with point forecasts. In this paper, a newly introduced method called Lower Upper Bound Estimation (LUBE) (Khosravi et al., 2011, [1]) is applied and extended for construction of PIs. The...
Evolving artificial neural networks has attracted much attention among researchers recently, especially in the fields where plenty of data exist but explanatory theories and models are lacking or based upon too many simplifying assumptions. Financial time series forecasting is one of them. A hybrid model is used to forecast the hourly electricity price from the California Power Exchange. A collaborative...
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