Solar Radiation Estimation Models Based on Artificial Intelligence Applied to the Photovoltaic Electrical Generation for Norte De Santander, Colombia
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SOLAR RADIATION ESTIMATION MODELS BASED ON ARTIFICIAL INTELLIGENCE APPLIED TO THE PHOTOVOLTAIC ELECTRICAL GENERATION FOR NORTE DE SANTANDER, COLOMBIA A Master's Thesis Submitted to the Faculty of the Escola Tècnica d'Enginyeria de Telecomunicació de Barcelona Universitat Politècnica de Catalunya by Mario Joaquin Illera Bustos In partial fulfilment of the requirements for the degree of MASTER IN ELECTRONIC ENGINEERING Advisor: Francisco Juan Guinjoan Gispert Co-director: Sergio Basilio Sepúlveda Mora Barcelona, January 2019 Abstract The absence of direct measurements of solar radiation in many countries around the world (due to the high costs of installation and maintenance of the measuring devices) has been identified (in the Colombian case by the Energy Mining Planning Unit) as one of the main barriers for the deployment of photovoltaic systems. In this sense, estimation techniques have been developed in the literature for locations where this variable is not measured. These techniques take advantage of the correlation between the irradiance and other climatic parameters of wider distribution and easier access to construct models that forecast with high accuracy the solar potential of a specific place. Thus, the current research exposes the implementation of an indirect estimation model designed with Artificial Intelligence that uses the temperature, humidity, wind speed and sunshine duration to predict the irradiation, as a tool for sizing photovoltaic systems in Norte de Santander (Colombia) where solar radiation measurements are available just in three of the 40 municipalities in which the region is geographically divided. Table of Contents Chapter 1 - Introduction and objectives .................................................................................... 8 Chapter 2 - Irradiation indirect estimation models: State of art ............................................ 11 2.1. Statistical models ................................................................................................................ 11 2.2. Artificial Intelligence ............................................................................................................ 11 2.3. Physical models .................................................................................................................. 11 2.4. Empirical models ................................................................................................................ 12 2.4.1. Sunshine-based models .............................................................................................. 12 2.4.2. Cloud-based models .................................................................................................... 12 2.4.3. Temperature-based models ........................................................................................ 12 2.4.4. Other meteorological parameter-based models ......................................................... 13 2.5. Hybrid models .................................................................................................................... 13 Chapter 3 - Artificial Intelligence Techniques: ANN and ANFIS ............................................ 15 3.1. Artificial Neural Network .................................................................................................... 15 3.1.1. Overview ...................................................................................................................... 15 3.1.2. Topologies ................................................................................................................... 17 3.2. Adaptive-Network-based Fuzzy Inference System (ANFIS) ............................................. 21 3.2.1. Overview ...................................................................................................................... 21 3.2.1 Topology ....................................................................................................................... 22 Chapter 4 - Methodology ............................................................................................................ 26 4.1. Characteristics of the case study ........................................................................................ 26 4.1.1. Climatological information sources in Norte de Santander......................................... 27 4.1.2. Climatological information available in Norte de Santander ....................................... 30 4.2. Development of the model .................................................................................................. 33 4.2.1. Pre-processing stage ................................................................................................... 34 4.2.2. Design stage: Structure of the network ....................................................................... 47 Chapter 5 - Comparison for a specific case: PV sizing .......................................................... 73 5.1. Grid-connected PV system sizing ...................................................................................... 73 5.1.1. PV module selection .................................................................................................... 76 5.1.2. Selection of the inverter ............................................................................................... 76 5.2. Stand-alone PV system sizing ........................................................................................... 79 5.2.1. Numerical method........................................................................................................ 81 Chapter 6 - Expansion of the models ....................................................................................... 94 Chapter 7 - Conclusions and recommendations .................................................................... 97 Appendices ................................................................................................................................. 98 References ................................................................................................................................. 105 List of figures Figure 1. Distribution of studies with respect to the prediction technique.. ................................. 14 Figure 2. A three-layer artificial neural network ........................................................................... 16 Figure 3. ANN of three layers. ...................................................................................................... 18 Figure 4. One trajectory of execution of the backpropagation algorithm. ................................... 19 Figure 5. Back propagation of the error. ...................................................................................... 19 Figure 6. Updating of the weights. ............................................................................................... 19 Figure 7. Fuzzy inference system. ............................................................................................... 21 Figure 8. Commonly fuzzy if-then rules and fuzzy reasoning mechanisms. ............................... 22 Figure 9. Basic structure of ANFIS. ............................................................................................. 23 Figure 10. Common membership functions. ................................................................................ 24 Figure 11. Type 3 fuzzy reasoning (evaluation of the fuzzy inference). ...................................... 25 Figure 12. Mapping of a 2-input, type-3 ANFIS with three MF in each input. ............................. 25 Figure 13. Geographical characteristics of Norte de Santander. ................................................ 26 Figure 14. Distances from Cúcuta to different municipalities. ..................................................... 27 Figure 15. Meteorological stations for Norte de Santander. ........................................................ 33 Figure 16. Flowchart of the algorithm in Matlab for the first step of the preprocessing. ............. 36 Figure 17. Flowchart of the algorithm in Matlab for the second step of the pre-processing ....... 37 Figure 18. Standardized residual process ................................................................................... 40 Figure 19. Flowchart of the standardized residual algorithm implemented in Matlab. ................ 41 Figure 20. Polynomial regression for a set of irradiation data. .................................................... 42 Figure 21. Graphical representation of the standardizing process.............................................. 44 Figure 22. Flowchart for Chauvenet’s criterion algorithm in Matlab. ........................................... 45 Figure 23. ANN structure with the best performance for the UFPS Station. ............................... 55 Figure 24. Characteristics of the training process. ...................................................................... 55 Figure 25. Measured vs. Estimated data for hourly estimation model in the city of Cúcuta. ...... 58 Figure 26. Measured vs. Estimated data for hourly estimation model in the city of Pamplona. 58 Figure 27. Measured vs. Estimated data for hourly estimation model in the city of Herrán. ...... 59 Figure 28. Measured vs. Estimated data for daily estimation model in the city of Cúcuta. ........ 61 Figure 29. Measured vs. Estimated data for daily estimation model in the city of Pamplona. ... 62 Figure 30. Measured vs. Estimated data for daily estimation model in the