Abstract
Electricity demand has been rising significantly over the past few years, making it crucial to integrate renewable energy sources (RES) into power networks on a wide scale. Among the most popular alternative energy sources with very high potential is wind energy. However, there is significant variability in wind speed, which results in significant fluctuations in the electricity production from the wind energy. As a result, it is challenging to integrate RES technology and especially wind energy into electricity networks. More accurate forecasts are necessary for the efficient operation of RES power plants, as well as the provision of high-quality electricity at the most affordable prices and the secure and stable operation of electrical networks. Four deep machine learning (ML) algorithms, i.e. multi-head CNN, hybrid quantum multi-head CNN, multi-channel CNN, and encoder–decoder LSTM were applied in this study to estimate medium-term (24 h ahead) wind speed using a real-time measurement dataset.
This work received financial support from the project “Enhancing resilience of Cretan power system using distributed energy resources (CResDER)” (Proposal ID: 03698) financed by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the Action “2nd Call for H.F.R.I. Research Projects to support Faculty Members and Researchers”.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Begam KM, Deepa S (2019) Optimized nonlinear neural network architectural models for multistep wind speed forecasting. Comput Electr Eng 78:32–49
Bellinguer K, Girard R, Bontron G, Kariniotakis G (2020) Short-term forecasting of photovoltaic generation based on conditioned learning of geopotential fields. In Proceedings of the 55th international universities power engineering conference—virtual conference UPEC 2020—“Verifying the Targets”, Torino, Italy; 1–6, 1–4 September
Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S (2017) Quantum machine learning. Nature 549(7671):195–202
Blazakis K, Katsigiannis Y, Stavrakakis G (2022) One-day-ahead solar irradiation and windspeed forecasting with advanced deep learning techniques. Energies 15(12):4361
Bo G, Keke L, Hongtao Z, Jinhua Z, Hui H (2021) Short-term forecasting and uncertainty analysis of wind power. J Solar Energy Eng 143:054503
Brahimi T (2019) Using artificial intelligence to predict wind speed for energy application in Saudi Arabia. Energies 12(24):4669
Brownlee J (2018) Deep learning for time series forecasting: predict the future with MLPs, CNNs and LSTMs. In: Python; machine learning mastery: New York, NY, USA
Duan J, Zuo H, Bai Y, Duan J, Chang M, Chen B (2021) Short-term wind speed forecasting using recurrent neural networks with error correction. Energy 217:119397
Gan BY, Leykam D, Angelakis D (2022) Quantum machine learning with linear optics and coherent states. In: APS March meeting abstracts, W37-011
Husein M, Chung IY (2016) Day-ahead solar irradiance forecasting for microgrids using a long short-term memory recurrent neural network: a deep learning approach. Energies 12(10):1856
Karatzoglou A (2019) Multi-channel convolutional neural networks for handling multi-dimensional semantic trajectories and predictingfuture semantic locations. International workshop on multiple-aspect analysis of semantic trajectories; Springer: Cham, Switzerland, 117–132
Kariniotakis GN, Stavrakakis GS, Nogaret EF (1996) Wind power forecasting using advanced neural networks models. IEEE Trans Energy Convers 11(4):762–767
Koutsoukas A, Monaghan KJ, Li X, Huan J (2017) Deep-learning: Investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data. J Cheminformatics 9(1):1–13
Kwon DH, Kim JB, Heo JS, Kim CM, Han YH (2019) Time series classification of cryptocurrency price trend based on a recurrent LSTM neural network. J Inf Process Syst 15(3):694–706
Leykam D, Angelakis DG (2023) Topological data analysis and machine learning. Adv Phys X 8(1):2202331
Li Y, Wu H, Liu H (2018a) Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction. Energy Convers Manag 167:203–219
Li C, Xiao Z, Xia X, Zou W, Zhang C (2018b) A hybrid model based on synchronous optimization for multi-step short-term wind speed forecasting. Appl Energy 215:131–144
Liu H, Mi X, Li Y (2018a) Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM. Energy Convers Manag 159:54–64
Liu H, Mi X-W, Li Y-F (2018b) Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network. Energy Convers Manag 156:498–514
Liu H, Duan Z, Chen C, Wu H (2019) A novel two-stage deep learning wind speed forecasting method with adaptive multiple error corrections and bivariate Dirichlet process mixture model. Energy Convers Manag 199:111975
Liu Z, Jiang P, Wang J, Zhang L (2021) Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm. Expert Syst Appl 177:114974
Lv SX, Wang L (2022) Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization. Appl Energy 311:118674
Neshat M, Nezhad MM, Abbasnejad E, Mirjalili S, Tjernberg LB, Garcia DA, Wagner M (2021) A deep learning-based evolutionary model for short-term wind speed forecasting: a case study of the Lillgrund offshore wind farm. Energy Convers Manag 236:114002
Neshat M, Nezhad MM, Mirjalili S, Piras G, Garcia DA (2022) Quaternion convolutional long short-term memory neural model with an adaptive decomposition method for wind speed forecasting: North aegean islands case studies. Energy Convers Manag 259:115590
Nezhad MM, Groppi D, Marzialetti P, Fusilli L, Laneve G, Cumo F, Garcia DA (2019) Wind energy potential analysis using sentinel-1 satellite: a review and a case study on Mediterranean islands, Renew. Sust. Energy Rev. 109:499–513
Nielsen MA, Chuang IL (2010) Quantum computation and quantum information. Cambridge University Press
Pareek V, Chaudhury S (2021) Deep learning-based gas identification and quantification with auto-tuning of hyper-parameters. Soft Comput 25(22):14155–14170
Phillipson F (2020) Quantum machine learning: benefits and practical examples. QANSWER, 51–56
Potter CW, Negnevitsky M (2006) Very short-term wind forecasting for Tasmanian power generation. IEEE Trans Power Syst 21(2):965–972
Preskill J (2018) Quantum computing in the NISQ era and beyond. Quantum 2:79
Schetakis N, Aghamalyan D, Griffin P et al (2022b) Review of some existing QML frameworks and novel hybrid classical–quantum neural networks realising binary classification for the noisy datasets. Sci Rep 12:11927
Schetakis N, Aghamalyan D, Boguslavsky M, Rees A, Rakotomalala M, Griffin PR (2022) Quantum machine learning for credit scoring. Res Collection School Comput Inf Syst, 1–13
Schuld M, Sinayskiy I, Petruccione F (2015) An introduction to quantum machine learning. Contemp Phys 56(2):172–185
Singh SN, Mohapatra A (2019) Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting. Renew Energy 136:758–768
Soman SS, Zareipour H, Malik O, Mandal P (2010) A review of wind power and wind speed forecasting methods with different time horizons. In: North American Power Symp, 1–8
Stavrakakis GS (2020) Guest editor: alternative sources of energy modeling and automation. MDPI-multidisciplinary digital publishing institute
Suradhaniwar S, Kar S, Durbha SS, Jagarlapudi A (2021) Time series forecasting of univariateagrometeorological data: a comparative performance evaluation via one-step and multi-step ahead forecasting strategies. Sensors 21(7):2430
Suresh V, Janik P, Rezmer J, Leonowicz Z (2020) Forecasting solar PV output using convolutional neural networks with a sliding window algorithm. Energies 13(3):723
Tian C, Hao Y, Hu J (2018) A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization. Appl Energy 231:301–319
Viet DT, Phuong VV, Duong MQ, Tran QT (2020) Models for short-term wind power forecasting based on improved artificial neural network using particle swarm optimization and genetic algorithms. Energies 13(11):2873
Wang Y, Wu L (2016) On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation. Energy 112:208–220
Wang J, Song Y, Liu F, Hou R (2016) Analysis and application of forecasting models in wind power integration: a review of multi-step-ahead wind speed forecasting models. Renew Sustain Energy Rev 60:960–981
Wang J, Zhang N, Lu H (2019) A novel system based on neural networks with linear combination framework for wind speed forecasting. Energy Convers Manag 181:425–442
Wang Y, Zou R, Liu F, Zhang L, Liu Q (2021) A review of wind speed and wind power forecasting with deep neural networks. Appl Energy 304:117766
Wu B, Wang L, Zeng YR (2022) Interpretable wind speed prediction with multivariate time series and temporal fusion transformers. Energy 252:123990
Zhang D, Peng X, Pan K, Liu Y (2019) A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine. Energy Convers Manag 180:338–357
Zhang Y, Pan G, Chen B, Han J, Zhao Y, Zhang C (2020) Short-term wind speed prediction model based on GA-ANN improved by VMD. Renew Energy 156:1373–1388
Zhou Q, Wang C, Zhang G (2019) Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems. Appl Energy 250:1559–1580
Zhu A, Li X, Mo Z, Wu H (2017) Wind power prediction based on a convolutional neural network. In Proceedings of the international conference on circuits, devices and systems, Tibet Hotel Chengdu, Chengdu, China,; 133–135, 5–8
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Blazakis, K., Katsigiannis, Y., Schetakis, N., Stavrakakis, G. (2024). One-Day-Ahead Wind Speed Forecasting Based on Advanced Deep and Hybrid Quantum Machine Learning. In: Farmanbar, M., Tzamtzi, M., Verma, A.K., Chakravorty, A. (eds) Frontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications. FAIEMA 2023. Frontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications. Springer, Singapore. https://doi.org/10.1007/978-981-99-9836-4_13
Download citation
DOI: https://doi.org/10.1007/978-981-99-9836-4_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9835-7
Online ISBN: 978-981-99-9836-4
eBook Packages: Business and ManagementBusiness and Management (R0)