Development of a model for short time solar data (5 min and hourly).
Optimization of the method and very good adequacy.
Research of the best meteorological input combinations by exhaustive tests and Pearson Coefficient utilization.
Only one thousand stations around the world measures solar radiation sometimes with a poor quality. The objective of this paper is to show if solar irradiations at short time scale, hourly and 5-min, (very under-studied time-step) can be estimated from more available and cheaper data using Artificial Neural Networks. 7 meteorological and 3 calculated parameters are used as inputs; 1023 inputs combinations are possible for each time-step; the best inputs combinations are pursued. A variable selection method based on Pearson’s coefficient is firstly used between inputs and between output and inputs; some inputs are redundant (particularly calculated ones) and/or with a weak link with solar radiation (as wind speed and direction), sunshine duration is strongly correlated with solar irradiation. The models have a good adequacy mainly with sunshine duration in the input set. For hourly data, the performances of the 6 and 10 inputs model are nRMSE = 13.90% (nMAE = 13.28%, R2 = 0.979) and nRMSE = 13.33% (nMAE = 12.72%, R2 = 0.9812); without sunshine duration, the model nRMSE (with 5 inputs) falls to 28.27%. For 5-min data, the 6 and 10 inputs models have a nRMSE equal to 19.35% and 18.65% which is very good for such a time-step. A comparison with literature highlighted the quality of these models.
- Solar irradiation;
- Artificial neural network;
- Short time step
Copyright © 2016 Elsevier Ltd. All rights reserved.