Skip to main content
SpringerLink
Log in

A New Method for Generating Short-Term Power Forecasting Based on Artificial Neural Networks and Optimization Methods for Solar Photovoltaic Power Plants

  • Chapter
  • First Online:
Book cover Solar Photovoltaic Power Plants

Part of the book series: Power Systems ((POWSYS))

Abstract

In recent times, solar PV power plants have been used worldwide due to their high solar energy potential. Although the PV power plants are highly preferred, the main disadvantage of the system is that the output power characteristics of the system are unstable. As PV power plant system is connected to the grid side, unbalanced power flow effects all systems controls. In addition, the load capacitys is not exactly known. For this reason, it has become an important issue to be known correctly in PV output power and their time-dependent changes. The main aim of this work is to eliminate power plant instability due to the output power imbalance. For the short-term, power prediction is estimated by real-time data of 1 MW PV power plant in use. Estimation power data are compared with real-time data and precision of the proposed method is demonstrated. In the first phase, traditional artificial intelligence algorithms are used. Then, these algorithms are trained with swarm based optimization methods and the performance analyses are presented in detail. Among all the algorithms used, the algorithm with the lowest error is determined. Thus, this study provides useful information and techniques to help researchers who are interested in planning and modeling PV power plants.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Razak A, Irwan YM, Leow WZ, Irwanto M, Safwati I, Zhafarina M (2016) Investigation of the effect temperature on photovoltaic (PV) panel output performance. Int J Adv Sci Eng Inf Technol 6(5):682–688

    Article  Google Scholar 

  2. Chang W-Y (2014) A literature review of wind forecasting methods. J Power Energy Eng 2(4)

    Google Scholar 

  3. Mellit A, Pavan AM (2010) A 24-h forecast of solar radiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy. Sol Energy 84(5):807–821

    Article  Google Scholar 

  4. Sperati S, Alessandrini S, Delle Monache L (2016) An application of the ECMWF ensemble prediction system for short-term solar power forecasting. Sol Energy 133:437–450

    Article  Google Scholar 

  5. Ahmad A, Anderson TN, Lie TT (2015) Hourly global solar radiation forecasting for New Zealand. Sol Energy 122:1398–1408

    Article  Google Scholar 

  6. David M, Ramahatana F, Trombe P-J, Lauret P (2016) Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models. Solar Energy 133:55–72

    Article  Google Scholar 

  7. Izgi E, Öztopal A, Yerli B, Kaymak MK, Şahin AD (2012) Short-mid-term solar power prediction by using artificial neural networks. Sol Energy 86(2):725–733

    Article  Google Scholar 

  8. Kardakos EG, Alexiadis MC, Vagropoulos SI, Simoglou CK, Biskas PN, Bakirtzis AG (2013) Application of time series and artificial neural network models in short-term forecasting of PV power generation, In: 2013 48th international universities power engineering conference (UPEC), pp 1–6

    Google Scholar 

  9. Persson C, Bacher P, Shiga T, Madsen H (2017) Multi-site solar power forecasting using gradient boosted regression trees. Sol Energy 150:423–436

    Article  Google Scholar 

  10. e Silva RA, Brito MC (2018) Impact of network layout and time resolution on spatio-temporal solar forecasting. Sol Energy 163:329–337

    Article  Google Scholar 

  11. Chen SX, Gooi HB, Wang MQ (2013) Solar radiation forecast based on fuzzy logic and neural networks. Renew Energy 60:195–201

    Article  Google Scholar 

  12. Ramaswamy S, Sadhu PK (2017) Forecasting PV power from solar irradiance and temperature using neural networks. In: 2017 international conference on infocom technologies and unmanned systems (trends and future directions)(ICTUS), pp 244–248

    Google Scholar 

  13. Dewangan CL, Singh SN, Chakrabarti S (2017) Solar irradiance forecasting using wavelet neural network. In: Asia-Pacific power and energy engineering conference (APPEEC). IEEE PES, pp 1–6

    Google Scholar 

  14. Raza MQ, Nadarajah M, Ekanayake C (2017) A multivariate ensemble framework for short term solar photovoltaic output power forecast. In: Power and energy society general meeting. IEEE, pp 1–5

    Google Scholar 

  15. Percy SD, Aldeen M, Berry AM (2018) Residential demand forecasting with solar-battery systems: a survey-less approach. IEEE Trans Sustain Energy

    Google Scholar 

  16. Alzahrani A, Shamsi P, Ferdowsi M, Dagli C (2017) Solar irradiance forecasting using deep recurrent neural networks. In: 2017 IEEE 6th international conference on renewable energy research and applications (ICRERA), pp 988–994

    Google Scholar 

  17. Wang Y, Shen Y, Mao S, Cao G, Nelms RM (2018) Adaptive learning hybrid model for solar intensity forecasting. IEEE Trans Ind Inform 14(4):1635–1645

    Article  Google Scholar 

  18. Praynlin E, Jensona JI (2017) Solar radiation forecasting using artificial neural network. In: 2017 innovations in power and advanced computing technologies (i-PACT), pp 1–7

    Google Scholar 

  19. Çevik S, Çakmak R, Altaş İH (2017) A day ahead hourly solar radiation forecasting by artificial neural networks: a case study for Trabzon province. In: 2017 international artificial intelligence and data processing symposium (IDAP), pp 1–6

    Google Scholar 

  20. Sahoo AK, Sahoo SK (2016) Energy forecasting for grid connected MW range solar PV system. In: 2016 7th India international conference on power electronics (IICPE), pp 1–6

    Google Scholar 

  21. Majumder I, Behera MK, Nayak N (2017) Solar power forecasting using a hybrid EMD-ELM method. In: 2017 international conference on circuit, power and computing technologies (ICCPCT). pp 1–6

    Google Scholar 

  22. Hassan S, Khanesar MA, Hajizadeh A, Khosravi A (2017) Hybrid multi-objective forecasting of solar photovoltaic output using Kalman filter based interval type-2 fuzzy logic system. In: 2017 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1–6

    Google Scholar 

  23. Severiano CA, Silva PC, Sadaei HJ, Guimarães FG (2017) Very short-term solar forecasting using fuzzy time series. In: 2017 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1–6

    Google Scholar 

  24. Orjuela-Cañón AD, Hernández J, Rivero CR (2017) Very short term forecasting in global solar irradiance using linear and nonlinear models. In: 2017 IEEE workshop on power electronics and power quality applications (PEPQA), pp 1–5

    Google Scholar 

  25. Chiang P-H, Chiluvuri SPV, Dey S, Nguyen TQ (2017) Forecasting of solar photovoltaic system power generation using wavelet decomposition and bias-compensated random forest. In: Ninth annual IEEE green technologies conference (GreenTech), pp 260–266

    Google Scholar 

  26. Moustris KP, Kavvadias KA, Kokkosis AI, Paliatsos AG (2016) One day-ahead forecasting of mean hourly global solar radiation for energy management systems purposes using artificial neural network modeling

    Google Scholar 

  27. Gensler A, Henze J, Sick B, Raabe N (2016) Deep learning for solar power forecasting–an approach using AutoEncoder and LSTM neural networks. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC), pp 002858–002865

    Google Scholar 

  28. Huang C, Zhang Z, Bensoussan A (2016) Forecasting of daily global solar radiation using wavelet transform-coupled Gaussian process regression: case study in Spain. In: Innovative smart grid technologies-Asia (ISGT-Asia). IEEE 2016, pp 799–804

    Google Scholar 

  29. Sheng H, Xiao J, Cheng Y, Ni Q, Wang S (2018) Short-term solar power forecasting based on weighted Gaussian process regression. IEEE Trans Ind Inform 65(1):300–308

    Article  Google Scholar 

  30. Sivaneasan B, Yu CY, Goh KP (2017) Solar forecasting using ANN with fuzzy logic pre-processing. Energy Procedia 143:727–732

    Article  Google Scholar 

  31. Watetakarn S, Premrudeepreechacharn S (2015) Forecasting of solar irradiance for solar power plants by artificial neural network. In: Innovative smart grid technologies-Asia (ISGT ASIA). IEEE, 2015, pp 1–5

    Google Scholar 

  32. Behera MK, Majumder I, Nayak N (2018) Solar photovoltaic power forecasting using optimized modified extreme learning machine technique. Eng Sci Technol Int J

    Google Scholar 

  33. Verma T, Tiwana APS, Reddy CC, Arora V, Devanand P (2016) Data analysis to generate models based on neural network and regression for solar power generation forecasting. In: 2016 7th international conference on intelligent systems, modelling and simulation (ISMS), pp 97–100

    Google Scholar 

  34. Gairaa K, Chellali F, Benkaciali S, Messlem Y, Abdallah K (2015) Daily global solar radiation forecasting over a desert area using NAR neural networks comparison with conventional methods. In: 2015 international conference on renewable energy research and applications (ICRERA), pp 567–571

    Google Scholar 

  35. Silva F, Teixeira B, Teixeira N, Pinto T, Praça I, Vale Z (2016) Application of a hybrid neural fuzzy inference system to forecast solar intensity. In: 2016 27th international workshop on database and expert systems applications (DEXA), pp 161–165

    Google Scholar 

  36. Alfadda A, Adhikari R, Kuzlu M, Rahman S (2017) Hour-ahead solar PV power forecasting using SVR based approach. In: Power and energy society innovative smart grid technologies conference (ISGT). IEEE, 2017, pp 1–5

    Google Scholar 

  37. Abuella M, Chowdhury B (2015) Solar power forecasting using artificial neural networks. In: North American power symposium (NAPS). 2015, pp 1–5

    Google Scholar 

  38. Sreekumar S, Sharma KC, Bhakar R (2016) Optimized support vector regression models for short term solar radiation forecasting in smart environment. In: Region 10 conference (TENCON). IEEE, 2016, pp 1929–1932

    Google Scholar 

  39. Ghayekhloo M, Ghofrani M, Menhaj MB, Azimi R (2015) A novel clustering approach for short-term solar radiation forecasting. Sol Energy 122:1371–1383

    Article  Google Scholar 

  40. Mellit A, Sağlam S, Kalogirou SA (2013) Artificial neural network-based model for estimating the produced power of a photovoltaic. Renew Energy 60:71–78

    Article  Google Scholar 

  41. Notton G, Paoli C, Ivanova L, Vasileva S, Nivet ML (2013) Neural network approach to estimate 10-min solar global irradiation values on tilted planes. Renew Energy 50:576–584

    Article  Google Scholar 

  42. Yona A, Senjyu T, Funabashi T, Kim C-H (2013) Determination method of insolation prediction with fuzzy and applying neural network for long-term ahead PV power output correction. IEEE Trans Sustain Energy 4(2):527–533

    Article  Google Scholar 

  43. Amrouche B, Le Pivert X (2014) Artificial neural network based daily local forecasting for global solar radiation. Appl Energy 130:333–341

    Article  Google Scholar 

  44. Almonacid F, Pérez-Higueras PJ, Fernández EF, Hontoria L (2014) A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator. Energy Convers Manag 85:389–398

    Article  Google Scholar 

  45. Dahmani K, Dizene R, Notton G, Paoli C, Voyant C, Nivet ML (2014) Estimation of 5-min time-step data of tilted solar global irradiation using ANN (Artificial Neural Network) model. Energy 70:374–381

    Article  Google Scholar 

  46. Kaushika ND, Tomar RK, Kaushik SC (2014) Artificial neural network model based on interrelationship of direct, diffuse and global solar radiations. Sol Energy 103:327–342

    Article  Google Scholar 

  47. Kashyap Y, Bansal A, Sao AK (2015) Solar radiation forecasting with multiple parameters neural networks. Renew Sustain Energy Rev 49:825–835

    Article  Google Scholar 

  48. Teo TT, Logenthiran T, Woo WL (2015) Forecasting of photovoltaic power using extreme learning machine. In: Innovative smart grid technologies-Asia (ISGT ASIA). IEEE, 2015, pp 1–6

    Google Scholar 

  49. Dong Z, Yang D, Reindl T, Walsh WM (2014) Satellite image analysis and a hybrid ESSS/ANN model to forecast solar irradiance in the tropics. Energy Convers Manag 79:66–73

    Article  Google Scholar 

  50. Quan DM, Ogliari E, Grimaccia F, Leva S, Mussetta M (2013) Hybrid model for hourly forecast of photovoltaic and wind power. In: 2013 IEEE international conference on fuzzy systems (FUZZ), pp 1–6

    Google Scholar 

  51. Bouzerdoum M, Mellit A, Pavan AM (2013) A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant. Sol Energy 98:226–235

    Article  Google Scholar 

  52. Gandelli A, Grimaccia F, Leva S, Mussetta M, Ogliari E (2014) Hybrid model analysis and validation for PV energy production forecasting. In: 2014 international joint conference on neural networks (IJCNN), pp 1957–1962

    Google Scholar 

  53. Tuohy A et al (2015) Solar forecasting: methods, challenges, and performance. IEEE Power Energy Mag 13(6):50–59

    Article  Google Scholar 

  54. Das UK et al (2018) Forecasting of photovoltaic power generation and model optimization: a review. Renew Sustain Energy Rev 81:912–928

    Article  Google Scholar 

  55. Debnath KB, Mourshed M (2018) Forecasting methods in energy planning models. Renew Sustain Energy Rev 88:297–325

    Article  Google Scholar 

  56. Yano F, Shohdohji T, Toyoda Y (2007) An improvement of particle swarm optimization with a neighborhood search algorithm. Ind Eng Manag Syst 6(1):64–71

    Google Scholar 

  57. Ab Wahab MN, Nefti-Meziani S, Atyabi A (2015) A comprehensive review of swarm optimization algorithms. PLoS One 10(5):e0122827

    Article  Google Scholar 

  58. Dada EG, Ramlan EI (2015) Primal-dual interior point method particle swarm optimization (pdipmPSO) algorithm. In: 3rd international conference on advances in engineering sciences and applied mathematics (ICAESAM’2015), London (UK), 23–24 March 2015. https://doi.org/10.15242/iie.e0315072,2015

Download references

Acknowledgements

The authors would like to acknowledge the Scientific Project Unit of Adana Science and Technology University (Project Number: 18103015 and 18103016) for full financial support.

Author information

Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Adana Science and Technology University, Adana, Turkey

    Tugce Demirdelen & Burak Esenboga

  2. Computer Engineering Department, Adana Science and Technology University, Adana, Turkey

    Inayet Ozge Aksu

  3. Electrical and Electronics Engineering Department, Cukurova University, Adana, Turkey

    Kemal Aygul

  4. Energy Systems Engineering Department, Adana Science and Technology University, Adana, Turkey

    Firat Ekinci

  5. Mechanical Engineering Department, Cukurova University, Adana, Turkey

    Mehmet Bilgili

Authors
  1. Tugce Demirdelen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Inayet Ozge Aksu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Burak Esenboga
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kemal Aygul
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Firat Ekinci
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mehmet Bilgili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tugce Demirdelen .

Editor information

Editors and Affiliations

  1. Faculty of Automation and Computers, Politehnica University of Timișoara, Timișoara, Romania

    Radu-Emil Precup

  2. Department of Electrical and Electronics Engineering, Sakarya University, Serdivan/Sakarya, Turkey

    Tariq Kamal

  3. School of Electrical Engineering, Chongqing University, Chongqing, China

    Syed Zulqadar Hassan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Demirdelen, T., Ozge Aksu, I., Esenboga, B., Aygul, K., Ekinci, F., Bilgili, M. (2019). A New Method for Generating Short-Term Power Forecasting Based on Artificial Neural Networks and Optimization Methods for Solar Photovoltaic Power Plants. In: Precup, RE., Kamal, T., Zulqadar Hassan, S. (eds) Solar Photovoltaic Power Plants. Power Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-6151-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6151-7_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6150-0

  • Online ISBN: 978-981-13-6151-7

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation