Multitime-Scale Data-Driven Spatio-Temporal Forecast of Photovoltaic Generation

Energy production forecasting in a PV plant using transfer function models

“…The results of our model were compared to a classical Persistence (PSS) Model which assumes that the forecasted energy production in the PV plant is the same as the measured value in the previous time step. The latter is usually adopted as a benchmark for new forecasting models [13].…”

“…The raised crucial problem is the strong dependence of the system response from many extrinsic factors, such as insolation intensity, ambient temperature, cell temperature, air velocity, humidity, cloudiness and pollution. All these factors have to be taken into account for the modelling of a PV plant [1]- [3], for the tracking of the Maximum Power Point (MPP) [4], [5], for the monitoring of the energy performance [6], [7], for the analysis and the modelling of the defects [8], [9], for the planning of the day after, for the forecasting purposes [10]- [13]. As reported in [13], PV generation forecasting methods can be broadly classified into three approaches: a) the Numerical Weather Prediction (NWP)-based forecast [14], which uses the first principles for predicting solar irradiance and PV generation; b) the data-driven statistical approach [15], [16], which includes Auto-Regression (AR)-based models and computational intelligence tools such as Artificial Neural Networks (ANNs) [9]; c) the hybrid one, which combines the NWP-based and data-driven models.…”

Notice of Removal Dynamic optimal power flow in transmission networks with high penetration of intermittent renewable energy sources

“…On the other hand, it is assumed in the analysis that the fluctuation in the PV power output can be predicted. In reality, forecasting the PV power output will include a forecast error [14]. The time lag (approximately 10 min) of the line-temperature increase will be utilized for the PV forecasting uncertainty.…”

Evaluation Method for Real-Time Dynamic Line Ratings Based on Line Current Variation Model for Representing Forecast Error of Intermittent Renewable Generation

“…In all these methods, it is critical to ensure the secure operation of power systems with IRESs. However, it has been noted that the distribution of the forecasting error is a heavy-tailed one instead of a normal one [7,8]. Therefore, even in the case of short-term predictions (e.g., those for less than 30 min) [9], extremely large forecasting errors must be considered during the operation of power systems, even though such errors occur rarely.…”