Author:
Giuseppe Nunnari
Affiliation:
Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, Universitá degli Studi di Catania, Viale A. Doria, 6, 95125 Catania and Italy
Keyword(s):
Clearness Index, Hidden Markov Models, Neural Network Models, Naive-Bayes Models, Surrogate Models, Persistent Model.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Energy Efficiency and Green Manufacturing
;
Health Engineering and Technology Applications
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge-Based Systems
;
Machine Learning in Control Applications
;
Neural Networks Based Control Systems
;
Production Planning, Scheduling and Control
;
Symbolic Systems
Abstract:
This paper deals with the problem of forecasting the class of the daily clearness index which can be relevant for PV applications. A large number of solar stations, publicly available, was processed by using five different approaches, namely, the feed-forward neural networks, the Hidden Markov models, the Naive-Bayes models, the Surrogate models and the Persistent models. Experimental results show that one-day ahead forecasting of the class of daily clearness can be reliable performed in a 2-class framework and with less accuracy in a 3-class framework. Furthermore, for this purpose, the HMM approach is recommended among the considered ones. The global performance of the class prediction models, evaluated by calculating the average confusion rate (CR), showed that using HMM models provide CR ≤ 0.3 for 2-class clustering classes, while, for the 3-class framework it rises to 0.35.