Nowadays, the world is shifting to renewable energy sources because of fossil fuels emissions which have a harmful impact on the environment. Despite of the large number of renewable energy sources, the use of wind energy and solar energy is increasing all over the world. The integration of wind and solar sources in generation of energy are quite a difficult task since various issues are created during the energy transaction process from generation to transmission. Wind and Solar sources depend upon various factors such as size of the turbine, topography, terrain and many other factors as well.
In recent years, many organizations, standards and pacts have been developed on an international level which leads to the degradation of global environment, lack of traditional energy and development of energy production from a source which is inexhaustible. This strategic development is the priority of all the countries. Obviously, wind and solar power which lie among renewable energy sources which are famous because of its energy output, clean power production, non-polluting etc. These are why wind and solar power production have been employed in energy markets with the reduction of costs. Also, the cost ratio between production and, implementation and maintenance has greatly affected by the maturity of the technology. Though, the stochastic behavior of wind and solar resources are responsible for the integration in the grid and its variable behavior throughout its lifetime which creates complications in the operation as well as management of the asset.
The above-mentioned behavior for energy production by wind farms or solar farms depend upon the annual wind speed, sunshine irradiance, conditions of weather, the capacity of wind and solar power, connection with the electrical grid, maintenance of wind schedule and ability of the grid to accept wind power when it is available in the grid.
The power that cannot be dispatched from a wind farm or solar farm to the electric grid may have an adverse effect on the quality as well as the balance of power as required by the system due to which the profit of wind or solar farm owners will be compromised. However, these issues are taken into consideration by many researchers and energy markets which are being presented as a new idea of predicting wind and solar power and energy production by the mentioned resources. In this way, the potential for energy production can be properly monitored and the optimization of wind or solar power resources are carried out. In this thesis, timescales have been used for the classification of the methods for forecasting of wind and solar power and the idea with pros and cons of several methods. Also, this thesis surveys a forecasting methods and techniques for improvement of efficiency and ramp events. Many countries including USA, China, Germany, Nordic countries, UK, Denmark, Ireland and other countries are forecasting wind to avoid any disturbance in economic dispatch and fulfillment of unit commitment. This thesis provides the information related to the improvement of methods and efficiency in different types of forecasting techniques and the recommendations are given to advance a method with further research and development.

Table of Content
PAGE
Acknowledgement i
Abstract ii
Table of Content iv
List of Figures vii
List of Tables ix
Chapter 1 Introduction 1
1.1 Background 1
1.2 Organization of Thesis 2
1.3 Contributions of this Thesis 3
Chapter 2 Literature Review 4
2.1 Theory 4
2.1.1 Wind Forecasting Methods 4
2.1.2 Concept of Measurement Accuracy 4
2.1.3 Accuracy of Wind forecasting 10
2.1.4 Wind power Forecasting by Academic Research and Industrial Application 15
2.1.5 Methods of Deterministic Wind Power Forecasting 16
2.1.6 Handling Uncertainties in Power System 17
Chapter 3 Methods and experience of wind power forecasts 18
3.1 Wind Forecasting 18
3.1.1 Physical Methods 18
3.1.2 Statistical Methods 18
3.1.3 Hybrid Methods 19
3.1.4 Artificial Intelligence Methods 20
3.1.5 Persistence method 21
3.1.6 Spatial Correlation Models 21
3.2 Research Work on the methods 22
3.2.1 Probabilistic Forecasting 22
3.2.2 Parametric and non-parametric Probabilistic Forecasting 24
3.2.3 Ramp Forecasting 27
3.2.4 Ramp forecasting experience 29
3.3 Summary research work on the methods 32
Chapter 4 Review of Wind and Solar Power in Selected Countries 33
4.1 Forecasting of Wind Energy by Countries 33
4.1.1 Forecasting of Wind Energy by Germany 33
4.1.2 Forecasting of Wind Energy by United Kingdom 33
4.1.3 Forecasting of Wind Energy by Denmark 34
4.1.4 Forecasting of Wind Energy by Nordic Countries 35
4.1.5 Forecasting of Wind Energy by Ireland 36
4.1.6 Forecasting of Wind Energy by USA 37
4.1.7 Forecasting of Wind Energy by Spain 42
4.1.8 Forecasting of Wind Energy by Australia 43
4.1.9 Comparison Table of forecasting of Wind Energy for different Countries 44
4.1.10 Summary of forecasting of Wind Energy 48
4.2 Forecasting of Solar Energy by Countries 48
4.2.1 Forecasting of Solar Energy by Germany 48
4.2.2 Forecasting of Solar Energy by USA 50
4.2.3 Forecasting of Solar Energy by UK 54
4.2.4 Forecasting of Solar Energy by Denmark 55
4.2.5 Forecasting of Solar Energy by China 57
4.2.6 Forecasting of Solar Energy by France 59
4.2.7 Forecasting of Solar Energy by Spain 62
4.2.8 Forecasting of Solar Energy by Australia 64
4.2.9 Comparison Table of Forecasting of Solar Energy for different Countries 66
4.2.10 Summary of forecasting of Solar Energy 69
Chapter 5 Conclusion and Discussion 70
5.1 Conclusion 70
5.2 Future Work 70
References 71
APPENDIXES 77
Appendix I: 2010 Year end Wind Power Capacity 77
Appendix II: Probabilistic Forecast 78
Appendix III: The Power Curve Model 80
Appendix IV : Wind Forecast Extra Information 82

 

References
[1] Standardizing the performance evaluation of short-term wind power prediction models. Wind Engineering, 2005. 29(6): p. 478-489.
[2] Short-term wind power forecasting using ridgelet neural network. Electric Power Systems Research, 2011. 81(12): p. 2099-2107.
[3]Madsen, H., et al., Standardizing the performance evaluation of short-term wind power prediction models. Wind engineering, 2005. 29(6): p. 475-489.
[4]Wu, Y.-K., P.-E. Su, and J.-S. Hong. An overview of wind power probabilistic forecasts. in 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). 2016. IEEE.
[5]Jung, J. and R.P. Broadwater, Current status and future advances for wind speed and power forecasting. Renewable and Sustainable Energy Reviews, 2014. 31: p. 762-777.
[6]Soman, S.S., et al. A review of wind power and wind speed forecasting methods with different time horizons. in North American Power Symposium 2010. 2010. IEEE.
[7]Marti, I., et al. Evaluation of advanced wind power forecasting models–results of the anemos project. 2006.
[8]Lange, M. and U. Focken, State-of-the-art in Wind Power Predictions in Germany and International Developments. 2006: Energymeteo.
[9]Lange, M. and D. Heinemann. Accuracy of short term wind power predictions depending on meteorological conditions. in CD-Proc. of the 2002 Global Windpower Conference, Paris, France. 2002.
[10]Lange, M. and D. Heinemann. Relating the uncertainty of short-term wind speed predictions to meteorological situations with methods from synoptic climatology. in Proceedings of the European Wind Energy Conference & Exhibition. 2003.
[11]Pinson, P. and G. Kariniotakis, On‐line assessment of prediction risk for wind power production forecasts. Wind Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology, 2004. 7(2): p. 119-132.
[12]Potter, C.W. and M. Negnevitsky, Very short-term wind forecasting for Tasmanian power generation. IEEE Transactions on Power Systems, 2006. 21(2): p. 965-972.
[13]Miranda, M.S. and R.W. Dunn. One-hour-ahead wind speed prediction using a Bayesian methodology. in 2006 IEEE Power Engineering Society General Meeting. 2006. IEEE.
[14]Milligan, M., M. Schwartz, and Y.-h. Wan, Statistical wind power forecasting models: Results for US wind farms. 2003, National Renewable Energy Lab.(NREL), Golden, CO (United States).
[15]Cadenas, E. and W. Rivera, Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks. Renewable Energy, 2009. 34(1): p. 274-278.
[16]Panteri, E. and S. Papathanassiou. Evaluation of two simple wind power forecasting models. in Proc. of the European Wind Energy Conf. 2008.
[17]Mori, H. and E. Kurata. Application of gaussian process to wind speed forecasting for wind power generation. in 2008 IEEE International Conference on Sustainable Energy Technologies. 2008. IEEE.
[18]Alexiadis, M., P. Dokopoulos, and H. Sahsamanoglou, Wind speed and power forecasting based on spatial correlation models. IEEE Transactions on Energy Conversion, 1999. 14(3): p. 836-842.
[19]Hocaoğlu, F.O., M. Fidan, and Ö.N. Gerek, Mycielski approach for wind speed prediction. Energy Conversion and Management, 2009. 50(6): p. 1436-1443.
[20]El-Fouly, T., E. El-Saadany, and M. Salama, Improved grey predictor rolling models for wind power prediction. IET Generation, Transmission & Distribution, 2007. 1(6): p. 928-937.
[21]Catalão, J.P.d.S., H.M.I. Pousinho, and V.M.F. Mendes. An artificial neural network approach for short-term wind power forecasting in Portugal. in 2009 15th International Conference on Intelligent System Applications to Power Systems. 2009. IEEE.
[22]Sideratos, G. and N.D. Hatziargyriou, An advanced statistical method for wind power forecasting. IEEE Transactions on power systems, 2007. 22(1): p. 258-265.
[23]Kavasseri, R.G. and K. Seetharaman, Day-ahead wind speed forecasting using f-ARIMA models. Renewable Energy, 2009. 34(5): p. 1388-1393.
[24]Lazić, L., G. Pejanović, and M. Živković, Wind forecasts for wind power generation using the Eta model. Renewable Energy, 2010. 35(6): p. 1236-1243.
[25]Fan, S., et al., Forecasting the wind generation using a two-stage network based on meteorological information. IEEE Transactions on Energy Conversion, 2009. 24(2): p. 474-482.
[26]Taylor, J.W., P.E. McSharry, and R. Buizza, Wind power density forecasting using ensemble predictions and time series models. IEEE Transactions on Energy Conversion, 2009. 24(3): p. 775-782.
[27]Barbounis, T.G., et al., Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Transactions on Energy Conversion, 2006. 21(1): p. 273-284.
[28]Salcedo-Sanz, S., et al., Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction. Renewable Energy, 2009. 34(6): p. 1451-1457.
[29]A Literature Review of Wind Forecasting Methods. Journal of Power and Energy Engineering, 2014. 2: p. 161-168.
[30]Current Methods and Advances in Forecasting of Wind Power Generation. Renewable energy, 2012. 37(1): p. 1-8.
[31]Very short-term wind forecasting for Tasmanian power generation. IEEE Transactions on Power Systems, 2006. 21(2): p. 965-972.
[32]Use of time-series analysis to model and forecast wind speed. Journal of Wind Engineering and Industrial Aerodynamics, 1995. 56(2-3): p. 311-322.
[33]Grey predictor for wind energy conversion systems output power prediction. IEEE Transactions on Power Systems, 2006. 21(3): p. 1450-1452.
[34]Wind Speed Forecasting Based on Second Order Blind Identification and Autoregressive Model. in 2010 Ninth International Conference on Machine Learning and Applications. 2010. Washington: IEEE.
[35]Statistical Wind Power Forecasting Models: Results for U.S Wind Farms. 17th Conference on Probability and Statistics in the Atmospheric Sciences, 2003: p. 1-17.
[36]The use of wavelet theory and ARMA model in wind speed prediction. in Proceedings of the 1st International Conference on Electric Power Equipment-Switching Technology. 2011. Xi'an.
[37] One-Hour-Ahead Wind Speed Prediction Using a Bayesian Methodology. in Proceedings of the 2006 IEEE Power Engineering Society General Meeting. June, 2006. Montreal: IEEE.
[38]A hybrid strategy of short term wind power prediction. Renewable Energy, 2013. 50: p. 590-595.
[39]Evaluation of Hybrid Forecasting Approaches for Wind Speed and Power Generation Time Series. in Renewable and Sustainable Energy Reviews. 2012.
[40]A Case Study on a Hybrid Wind Speed Forecasting Method Using BP Neural Network. Knowledge-Based Systems, 2011. 24: p. 1048-1056.
[41]Hybrid wavelet-PSO-ANFIS approach for short-term wind power forecasting in Portugal. IEEE Transactions on Sustainable Energy, 2011. 2(1): p. 50-59.
[42]Wind speed prediction in the mountainous region of India using an artificial neural network model. Renewable Energy, 2015. 80: p. 388-347.
[43]Review on probabilistic forecasting of wind power generation. in Renewable and Sustainable Energy Reviews. 2014. ELSEVIER.
[44]Probabilistic forecasting of wind power production losses in cold climates: a case study. European Academy of Wind Energy, 2018(3): p. 667-680.
[45]Deep learning based ensemble approach for probabilistic wind power forecasting. ELSEVIER, 2016: p. 56-70.
[46]Probabilistic forecasting of wind power ramp events using autoregressive logit models. European Journal of Operational Research, 2016: p. 703-712.
[47]Probability density forecasting of wind power using quantile regression neural network and kernel density estimation. Energy Conversion and Management, 2018. 164: p. 374-384.
[48]Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model. Applied Energy, 2017: p. 1097-1112.
[49]Direct Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power Generation. IEEE TRANSACTIONS ON POWER SYSTEMS, 2017. 32(4): p. 1-12.
[50]A review on the recent history of wind power ramp forecasting. Renewable and Sustainable Energy Reviews, 2015. 52: p. 1148-1157.
[51]Understanding wind ramp events through analysis of historical data. in IEEE PES T&D 2010. 2010. New Orleans, LA: IEEE.
[52]Detection and Statistics of Wind Power Ramps. IEEE TRANSACTIONS ON POWER SYSTEMS,, 2013. 28(4): p. 1-11.
53]Influence of wind power ramp rates in short-time wind power forecast error for highly aggregated capacity. IEEE, 2016.
[54]A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps. IEEE TRANSACTIONS ON SMART GRID, 2018: p. 1-13.
[55]Wind Power Ramp Event Forecasting Using a Stochastic Scenario Generation Method. IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2015. 06(02): p. 1-12.
[56]Wind power prediction in Germany–Recent advances and future challenges. Zeitschrift für Energiewirtschaft, 2006. 30(02): p. 155-120.
[57]Wind energy forecasting. CERC.
[58]Wind and Solar Power Production Forecasts. ConWX.
[59]MISO. MISO.
[60]Wind Power Forecasting in U.S. Electricity Markets. Electricity Journal, 2010. 23(03): p. 71-82.
[61]ERCOT in 2018: Challenges & Oppurtunities. 2018: Texas. p. 18.
[62]Power, M. Solar Power Forecast. 2017; Available from: http://www.meteopower.com/product-information/meteopower/solar-power-forecast.html.
[63]EWeLiNE. Development of Innovative Weather and Power Forecast Models for the Grid Integration of Weather Dependent Energy Sources Project (2012–2017). . 2017 20 June 2019]; Available from: http://www.projekt-eweline.de/en/project.html.
[64]Sobri, S., S. Koohi-Kamali, and N.A. Rahim, Solar photovoltaic generation forecasting methods: A review. Energy conversion and management, 2018. 156: p. 459-497.
[65]Lorenz, E., et al. Forecast of ensemble power production by grid-connected PV systems. in 20th European PV Conference. 2007. Milano.
[66]Remund, J., R. Perez, and E. Lorenz. Comparison of solar radiation forecasts for the USA. in Proc. of the 23rd European PV Conference. 2008. Valencia, Spain.
[67]Wolff, B., et al., Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data. Solar Energy, 2016. 135: p. 197-208.
[68]Massidda, L. and M. Marrocu, Use of Multilinear Adaptive Regression Splines and numerical weather prediction to forecast the power output of a PV plant in Borkum, Germany. Solar Energy, 2017. 146: p. 141-149.
[69]Orwig, K.D., et al., Recent trends in variable generation forecasting and its value to the power system. IEEE Transactions on Sustainable Energy, 2014. 6(3): p. 924-933.
[70]Porter, K. and J. Rogers, Status of centralized wind power forecasting in north america: May 2009-may 2010. 2010, National Renewable Energy Lab.(NREL), Golden, CO (United States).
[71]Freedman, J.M., et al., The Wind Forecast Improvement Project (WFIP): A public/private partnership for improving short term wind energy forecasts and quantifying the benefits of utility operations. The Southern Study Area, Final Report. 2014, AWS Truepower, LLC, Albany, NY (United States).
[72]Zhang, Y., et al., Day-ahead power output forecasting for small-scale solar photovoltaic electricity generators. IEEE Transactions on Smart Grid, 2015. 6(5): p. 2253-2262.
[73]Yang, D., P. Jirutitijaroen, and W.M. Walsh, Hourly solar irradiance time series forecasting using cloud cover index. Solar Energy, 2012. 86(12): p. 3531-3543.
[74]Gohari, M., et al., Comparison of solar power output forecasting performance of the Total Sky Imager and the University of California, San Diego Sky Imager. Energy Procedia, 2014. 49: p. 2340-2350.
[75]Dong, Z., et al., A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance. Energy, 2015. 82: p. 570-577.
[76]Bacher, P., H. Madsen, and H.A. Nielsen, Online short-term solar power forecasting. Solar Energy, 2009. 83(10): p. 1772-1783.
[77]Chen, C., et al., Online 24-h solar power forecasting based on weather type classification using artificial neural network. Solar energy, 2011. 85(11): p. 2856-2870.
[78]Shi, J., et al., Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Transactions on Industry Applications, 2012. 48(3): p. 1064-1069.
[79]Chen, J.-L., G.-S. Li, and S.-J. Wu, Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration. Energy conversion and management, 2013. 75: p. 311-318.
[80]Voyant, C., et al., Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation. Energy, 2011. 36(1): p. 348-359.
[81]Voyant, C., et al., Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation. Energy, 2012. 39(1): p. 341-355.
[82]Voyant, C., et al., Twenty four hours ahead global irradiation forecasting using multi‐layer perceptron. Meteorological Applications, 2014. 21(3): p. 644-655.
[83]Cros, S., et al. Extracting cloud motion vectors from satellite images for solar power forecasting. in 2014 IEEE Geoscience and Remote Sensing Symposium. 2014. IEEE.
[84]Amrouche, B. and X. Le Pivert, Artificial neural network based daily local forecasting for global solar radiation. Applied energy, 2014. 130: p. 333-341.
[85]Martín, L., et al., Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning. Solar Energy, 2010. 84(10): p. 1772-1781.
[86]Gala, Y., et al., Hybrid machine learning forecasting of solar radiation values. Neurocomputing, 2016. 176: p. 48-59.
[87]Almonacid, F., et al., A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator. Energy conversion and Management, 2014. 85: p. 389-398.
[88]Trapero, J.R., N. Kourentzes, and A. Martin, Short-term solar irradiation forecasting based on dynamic harmonic regression. Energy, 2015. 84: p. 289-295.
[89]Sanjari, M.J. and H. Gooi, Probabilistic forecast of PV power generation based on higher order Markov chain. IEEE Transactions on Power Systems, 2016. 32(4): p. 2942-2952.
[90]Huang, J., et al., Forecasting solar radiation on an hourly time scale using a Coupled AutoRegressive and Dynamical System (CARDS) model. Solar Energy, 2013. 87: p. 136-149.
[91]Rana, M., I. Koprinska, and V.G. Agelidis. Forecasting solar power generated by grid connected PV systems using ensembles of neural networks. in 2015 International Joint Conference on Neural Networks (IJCNN). 2015. IEEE.
[92]Do, T.P., F. Bourry, and X. Le Pivert. Assessment of storage and photovoltaic short-term forecast contribution to off-grid microgrid operation. in 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). 2017. IEEE.
[93]Wang, F., et al., Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters. Energies, 2012. 5(5): p. 1355-1370.
[94]van der Meer, D., Spatio-temporal probabilistic forecasting of solar power, electricity consumption and net load. 2018, Acta Universitatis Upsaliensis.
[95]Mandal, P., et al., Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques. Procedia Computer Science, 2012. 12: p. 332-337.
[96]Mathiesen, P. and J. Kleissl, Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States. Solar Energy, 2011. 85(5): p. 967-977.
[97]Pedro, H.T. and C.F. Coimbra, Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy, 2012. 86(7): p. 2017-2028.
[98]Bosch, J.L., Y. Zheng, and J. Kleissl, Deriving cloud velocity from an array of solar radiation measurements. Solar Energy, 2013. 87: p. 196-203.
[99]Yap, W.K. and V. Karri, Comparative study in predicting the global solar radiation for Darwin, Australia. Journal of Solar Energy Engineering, 2012. 134(3): p. 034501.
[100]Perez-Mora, N., V. Canals, and V. Martinez-Moll. Short-term Spanish aggregated solar energy forecast. in International Work-Conference on Artificial Neural Networks. 2015. Springer.
[101]Siegelmann, H.T., B.G. Horne, and C.L. Giles, Computational capabilities of recurrent NARX neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1997. 27(2): p. 208-215.
[102]Kraas, B., M. Schroedter-Homscheidt, and R. Madlener, Economic merits of a state-of-the-art concentrating solar power forecasting system for participation in the Spanish electricity market. Solar Energy, 2013. 93: p. 244-255.
[103]Kariniotakis, G., et al. Next Generation Short-Term Forecasting of Wind Power–Overview of the ANEMOS Project. in European Wind Energy Conference, EWEC 2006. 2006.
[104]Chowdhury, B.H. and S. Rahman. Forecasting sub-hourly solar irradiance for prediction of photovoltaic output. in 19th IEEE Photovoltaic Specialists Conference. 1987.
[105]Sfetsos, A. and A. Coonick, Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Solar Energy, 2000. 68(2): p. 169-178.
[106]Probability Interval Prediction of Wind Power Based on KDE Method With Rough Sets and Weighted Markov Chain. IEEE Access, 2018. 6: p. 51556-51566.
[107]Research and application of a hybrid model based on Meta learning strategy for wind power deterministic and probabilistic forecasting. Elsevier, 2018: p. 197-209.
[108]Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method. Elsevier, 2018: p. 211-216.
[109]Probabilistic Forecast for Multiple Wind Farms based on Regular Vine Copulas. IEEE TRANSACTIONS ON POWER SYSTEMS, 2018. 33(01): p. 1-12.
[110]A Distributed Approach for Wind Power Probabilistic Forecasting Considering Spatio-Temporal Correlation Without Direct Access to Off-Site Information. IEEE TRANSACTIONS ON POWER SYSTEMS, 2018. 33(05): p. 5714-5726.