Journal ArticleDOI
Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems
TLDR
An approach to predict regional PV power output based on forecasts up to three days ahead provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) and an approach to derive weather specific prediction intervals for irradiance forecasts are presented.Abstract:
The contribution of power production by photovoltaic (PV) systems to the electricity supply is constantly increasing. An efficient use of the fluctuating solar power production will highly benefit from forecast information on the expected power production. This forecast information is necessary for the management of the electricity grids and for solar energy trading. This paper presents an approach to predict regional PV power output based on forecasts up to three days ahead provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Focus of the paper is the description and evaluation of the approach of irradiance forecasting, which is the basis for PV power prediction. One day-ahead irradiance forecasts for single stations in Germany show a rRMSE of 36%. For regional forecasts, forecast accuracy is increasing in dependency on the size of the region. For the complete area of Germany, the rRMSE amounts to 13%. Besides the forecast accuracy, also the specification of the forecast uncertainty is an important issue for an effective application. We present and evaluate an approach to derive weather specific prediction intervals for irradiance forecasts. The accuracy of PV power prediction is investigated in a case study.read more
Citations
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Proceedings ArticleDOI
Multi-stage algorithm for uncertainty analysis of solar power forecasting
Shahrouz Alimohammadi,He Dawei +1 more
TL;DR: A new uncertainty quantification (UQ) algorithm for the error analysis of solar power forecasting is introduced and potentially evaluates the third party's forecast to increase the performance of VPP.
Journal ArticleDOI
Coupling sky images with radiative transfer models: a new method to estimatecloud optical depth
Felipe A. Mejia,Ben Kurtz,Keenan Murray,Laura M. Hinkelman,Manajit Sengupta,Yu Xie,Jan Kleissl +6 more
TL;DR: In this article, a method for retrieving cloud optical depth (τc) using a UCSD developed ground-based sky imager (USI) is presented, motivated from the analysis of simulated images of various τc produced by a radiative transfer model (RTM).
Journal ArticleDOI
Photo-voltaic power daily predictions using expanding PDE sum models of polynomial networks based on Operational Calculus
TL;DR: Differential Polynomial Neural Network (D-PNN) is a novel neuro-computing technique based on analogies with brain pulse signal processing that can model complex patterns without reducing significantly the data dimensionality as regression and soft-com computing methods do.
Journal ArticleDOI
Intra-day solar irradiation forecast using RLS filters and satellite images
TL;DR: This work explores the use of satellite information in a simpler way, namely spatial averages that require almost no preprocessing, and the models succeed to outperform a proposed optimal smart persistence, used here as an exigent performance benchmark.
Journal ArticleDOI
Deep Power Forecasting Model for Building Attached Photovoltaic System
TL;DR: A deep power forecasting model is proposed that employs a convolutional neural network to find the nonlinear relationship between meteorological information and BAPV power, while the data fed to the model are obtained through the 2-D Fourier transform of meteorological data.
References
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BookDOI
Forecast verification: a practitioner's guide in atmospheric science
TL;DR: Jolliffe et al. as mentioned in this paper proposed a framework for verification of spatial fields based on binary and categorical events, and proved the correctness of the proposed framework with past, present and future predictions.
Book
Statistical Intervals: A Guide for Practitioners
TL;DR: In this article, a detailed exposition of statistical intervals and emphasizes applications in industry is presented. But the discussion differentiates at an elementary level among different kinds of statistical interval and gives instruction with numerous examples and simple math on how to construct such intervals from sample data, including confidence intervals to contain a population percentile, confidence intervals on probability of meeting specified threshold value and prediction intervals to include observation in a future sample.
Evaluation of models to predict insolation on tilted surfaces
TL;DR: In this paper, an empirical study was performed to evaluate the validity of various insolation models which employ either an isotropic or an anisotropic distribution approximation for sky light when predicting insolation on tilted surfaces.