Intelligent Database Systems Lab Presenter: NENG-KAI, HONG Authors: HUAN LONG A, ZIJUN ZHANG A, ⇑, YAN SU 2014, APPLIED ENERGY Analysis of daily solar power prediction with data-driven approaches

Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments 1

Intelligent Database Systems Lab Motivation Various algorithms to date have been introduced to solar power prediction. However, there’s fewer researches to compare such algorithms. 2

Intelligent Database Systems Lab Objectives The objective to this paper is to perform a comparative analysis of four commonly considered algorithms including ANN, SVM, kNN and MLR in daily solar power prediction. 3

Intelligent Database Systems Lab Methodology 4 Parameter selection Step1. Classifying parameters Step2. Grouping parameters into clusters Step3. Applying parameter selection algorithm

Intelligent Database Systems Lab Methodology 5 1.Parameter importance analysis

Intelligent Database Systems Lab Methodology 6 2. Parameter selection process A C B

Intelligent Database Systems Lab Methodology 7 Solar power prediction models Multi-steps ahead predictions ‒S1 ‒S2

Intelligent Database Systems Lab Methodology 8 Parameter settings

Intelligent Database Systems Lab Experiment 9

Intelligent Database Systems Lab Experiment 10

Intelligent Database Systems Lab Conclusions None of the considered algorithms could consistently dominate other algorithms in the considered cases. 11

Intelligent Database Systems Lab Comments Advantage – Contributions to parameters selecting. Applications – Solar power prediction, Time-series model, Data mining, Artificial Neural Network, Support Vector Machine 12