A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval techniqueShort-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. Here, we introduce a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposes the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle are accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL).more »
Pioneer Design of Non-contact Synchronized Measurement Devices Using Electric and Magnetic Field SensorsHere, traditional synchrophasors rely on CTs and PTs physically connected to transmission lines or buses to acquire input signals for phasor measurement. However, it is challenging to install and maintain traditional phasor measurement units in some remote areas due to lack of facilities. Since transmission lines naturally generate alternating electrical and magnetic fields in the surrounding atmosphere, this paper presents two innovative designs for non-contact synchronized measurement devices (NCSMD), including an electric field sensor based non-contact SMD (E-NCSMD) and a magnetic field sensor based non-contact SMD (M-NCSMD). Compared with conventional synchrophasors, E-NCSMD and M-NCSMD are much more flexible to bemore »
A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimizationHere, this paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step ofmore »
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