1. E. López, C. Valle, H. Allende, E. Gil, and H. Madsen, “Wind power
forecasting based on echo state networks and long short-term memory,”
Energies, vol. 11, no. 3, p. 526, 2018.
2. B. C. G. De Aguiar and M. J. S. Valença, “Using reservoir computing for
forecasting of wind power generated by a wind farm,” in Proceedings of
the Sixth International Conference on Advanced Cognitive Technolo-
gies and Applications, Venice, Italy, pp. 25–29, Citeseer, 2014.
3. M. Drechsler, J. Egerer, M. Lange, F. Masurowski, J. Meyerhoff, and
M. Oehlmann, “Efﬁcient and equitable spatial allocation of renewable
power plants at the country scale,” Nature Energy, vol. 2, no. 9, pp. 1–9,
4. M. R. Shaner, S. J. Davis, N. S. Lewis, and K. Caldeira, “Geophysical
constraints on the reliability of solar and wind power in the united states,”
Energy & Environmental Science, vol. 11, no. 4, pp. 914–925, 2018.
5. R. E. Abdel-Aal, “Hourly temperature forecasting using abductive net-
works,” Engineering Applications of Artiﬁcial Intelligence, vol. 17, no. 5,
pp. 543–556, 2004.
6. J. Cifuentes, G. Marulanda, A. Bello, and J. Reneses, “Air temperature
forecasting using machine learning techniques: a review,” Energies,
vol. 13, no. 16, p. 4215, 2020.
7. G. Mihalakakou, M. Santamouris, and A. Tsangrassoulis, “On the
energy consumption in residential buildings,” Energy and buildings,
vol. 34, no. 7, pp. 727–736, 2002.
8. Ö. A. Dombaycı and M. Gölcü, “Daily means ambient temperature
prediction using artiﬁcial neural network method: A case study of
turkey,” Renewable Energy, vol. 34, no. 4, pp. 1158–1161, 2009.
9. A. Camia, G. Bovio, I. Aguado, and N. Stach, “Meteorological ﬁre dan-
ger indices and remote sensing,” Remote Sensing of Large Wildﬁres:
in the European Mediterranean Basin, pp. 39–59, 1999.
10. S. Malakar, S. Goswami, B. Ganguli, A. Chakrabarti, S. S. Roy,
K. Boopathi, and A. Rangaraj, “Designing a long short-term network
for short-term forecasting of global horizontal irradiance,” SN Applied
Sciences, vol. 3, pp. 1–15, 2021.
11. M. A. García and J. Balenzategui, “Estimation of photovoltaic module
yearly temperature and performance based on nominal operation cell
temperature calculations,” Renewable energy, vol. 29, no. 12, pp. 1997–
12. D. M. Smith, S. Cusack, A. W. Colman, C. K. Folland, G. R. Harris, and
J. M. Murphy, “Improved surface temperature prediction for the coming
decade from a global climate model,” science, vol. 317, no. 5839,
pp. 796–799, 2007.
13. “World meteorological organization,” Available online:
https://public.wmo.int/en/our-mandate/what-we-do (accessed on
1 February 2019), 2019.
14. A. E. Ben-Nakhi and M. A. Mahmoud, “Cooling load prediction for build-
ings using general regression neural networks,” Energy Conversion
and Management, vol. 45, no. 13-14, pp. 2127–2141, 2004.
15. S. C. Nwokolo, J. C. Ogbulezie, and A. U. Obiwulu, “Impacts of climate
change and meteo-solar parameters on photosynthetically active ra-
diation prediction using hybrid machine learning with physics-based
models,” Advances in Space Research, vol. 70, no. 11, pp. 3614–3637,
16. A. Alkesaiberi, F. Harrou, and Y. Sun, “Efﬁcient wind power prediction
using machine learning methods: A comparative study,” Energies,
vol. 15, no. 7, p. 2327, 2022.
17. Y. Mao and W. Shaoshuai, “A review of wind power forecasting &
prediction,” in 2016 International Conference on probabilistic methods
applied to power systems (PMAPS), pp. 1–7, IEEE, 2016.
18. N. A. Treiber, J. Heinermann, and O. Kramer, “Wind power prediction
with machine learning,” Computational sustainability, pp. 13–29, 2016.
19. T. Matsumoto and M. Endo, “One-week-ahead electricity price fore-
casting using weather forecasts, and its application to arbitrage in
the forward market: An empirical study of the japan electric power
exchange,” Journal of Energy Markets, vol. 14, no. 3, 2021.
20. L. M. Giordano and D. Morale, “A fractional brownian–hawkes model
for the italian electricity spot market: estimation and forecasting,” arXiv
preprint arXiv:1911.11795, 2019.
21. D. Schönheit, C. Dierstein, D. Möst, and L. Lorenz, “Zone-wide pre-
diction of generating unit-speciﬁc power outputs for electricity grid
congestion forecasts,” Journal of Energy Markets, vol. 14, no. 2, 2020.
22. Z. Hu, C. Li, Y. Cao, B. Fang, L. He, and M. Zhang, “How smart grid
contributes to energy sustainability,” Energy Procedia, vol. 61, pp. 858–
23. T. Logenthiran, D. Srinivasan, and A. M. Khambadkone, “Multi-agent
system for energy resource scheduling of integrated microgrids in a
distributed system,” Electric Power Systems Research, vol. 81, no. 1,
pp. 138–148, 2011.
24. V. N. Coelho, M. W. Cohen, I. M. Coelho, N. Liu, and F. G. Guimarães,
“Multi-agent systems applied for energy systems integration: State-of-
the-art applications and trends in microgrids,” Applied energy, vol. 187,
pp. 820–832, 2017.
25. I. Akhtar, S. Kirmani, and M. Jameel, “Reliability assessment of power
system considering the impact of renewable energy sources integration
into grid with advanced intelligent strategies,” IEEE Access, vol. 9,
pp. 32485–32497, 2021.
26. D. Tong, D. J. Farnham, L. Duan, Q. Zhang, N. S. Lewis, K. Caldeira,
and S. J. Davis, “Geophysical constraints on the reliability of solar and
wind power worldwide,” Nature communications, vol. 12, no. 1, p. 6146,
27. H. Holttinen, A. Tuohy, M. Milligan, E. Lannoye, V. Silva, S. Müller,
L. Sö, et al., “The ﬂexibility workout: managing variable resources and
assessing the need for power system modiﬁcation,” IEEE Power and
Energy Magazine, vol. 11, no. 6, pp. 53–62, 2013.
28. W. Dong and S. Li, “Reliability sensitivity of wind power system con-
sidering correlation of forecast errors based on multivariate nstpnt
method,” Protection and Control of Modern Power Systems, vol. 6,
no. 1, pp. 1–11, 2021.
29. M. A. Hossain, R. K. Chakrabortty, S. Elsawah, and M. J. Ryan, “Very
short-term forecasting of wind power generation using hybrid deep
learning model,” Journal of Cleaner Production, vol. 296, p. 126564,
30. H. Madsen, H. A. Nielsen, and T. S. Nielsen, “A tool for predicting the
wind power production of off-shore wind plants,” in Proceedings of the
Copenhagen Offshore Wind Conference & Exhibition, 2005.
31. S. Al-Deen, A. Yamaguchi, T. Ishihara, and R. Bessa, “A physical
approach to wind speed prediction for wind energy forecasting,” Journal
of Wind Engineering, vol. 108, pp. 349–352, 2006.
32. W.-Y. Chang et al., “A literature review of wind forecasting methods,”
Journal of Power and Energy Engineering, vol. 2, no. 04, p. 161, 2014.
33. F. Cassola and M. Burlando, “Wind speed and wind energy forecast
through kalman ﬁltering of numerical weather prediction model output,”
Applied energy, vol. 99, pp. 154–166, 2012.
34. S. Al-Deen, A. Yamaguchi, T. Ishihara, and R. Bessa, “A physical
approach to wind speed prediction for wind energy forecasting,” Journal
of Wind Engineering, vol. 108, pp. 349–352, 2006.
35. T. Ishihara, A. Yamaguchi, and Y. Fujino, “A nonlinear model mas-
cot: development and application,” in Proc. of 2003 European Energy
Conference and Exhibition, vol. 1, pp. 1–7, 2003.
36. L. Li, Y.-q. Liu, Y.-p. Yang, H. Shuang, and Y.-m. Wang, “A physical
approach of the short-term wind power prediction based on cfd pre-
calculated ﬂow ﬁelds,” Journal of Hydrodynamics, Ser. B, vol. 25, no. 1,
pp. 56–61, 2013.
37. L. Landberg, “Short-term prediction of local wind conditions,” Journal
of Wind Engineering and Industrial Aerodynamics, vol. 89, no. 3-4,
pp. 235–245, 2001.
38. U. Focken, M. Lange, and H.-P. Waldl, “Previento-a wind power pre-
diction system with an innovative upscaling algorithm,” in Proceedings
of the European Wind Energy Conference, Copenhagen, Denmark,
vol. 276, 2001.
39. Y. Liu, M. C. Roberts, and R. Sioshansi, “A vector autoregression
weather model for electricity supply and demand modeling,” Journal of
Modern Power Systems and Clean Energy, vol. 6, no. 4, pp. 763–776,
40. M.-D. Wang, Q.-R. Qiu, and B.-W. Cui, “Short-term wind speed forecast-
ing combined time series method and arch model,” in 2012 International
Conference on Machine Learning and Cybernetics, vol. 3, pp. 924–927,
41. H. Liu, H.-Q. Tian, C. Chen, and Y.-f. Li, “A hybrid statistical method to
predict wind speed and wind power,” Renewable energy, vol. 35, no. 8,
pp. 1857–1861, 2010.
42. E. Cadenas, W. Rivera, R. Campos-Amezcua, and C. Heard, “Wind
speed prediction using a univariate arima model and a multivariate
narx model,” Energies, vol. 9, no. 2, p. 109, 2016.
43. R. R. De Aquino, R. B. Souza, O. N. Neto, M. M. Lira, M. A. Carvalho,
and A. A. Ferreira, “Echo state networks, artiﬁcial neural networks and
fuzzy systems models for improve short-term wind speed forecasting,”
in 2015 International Joint Conference on Neural Networks (IJCNN),
pp. 1–8, IEEE, 2015.
44. W. Sun, M. Liu, and Y. Liang, “Wind speed forecasting based on feemd
and lssvm optimized by the bat algorithm,” Energies, vol. 8, no. 7,
pp. 6585–6607, 2015.
45. Q. Wu and C. Peng, “Wind power generation forecasting using least
squares support vector machine combined with ensemble empirical
mode decomposition, principal component analysis and a bat algo-
rithm,” Energies, vol. 9, no. 4, p. 261, 2016.
46. F. Bonanno, G. Capizzi, G. L. Sciuto, and C. Napoli, “Wavelet recurrent
neural network with semi-parametric input data preprocessing for micro-
wind power forecasting in integrated generation systems,” in 2015
International Conference on Clean Electrical Power (ICCEP), pp. 602–
609, IEEE, 2015.
47. D. Liu, J. Wang, and H. Wang, “Short-term wind speed forecasting
based on spectral clustering and optimised echo state networks,” Re-
newable Energy, vol. 78, pp. 599–608, 2015.
48. C. Sheng, J. Zhao, Y. Liu, and W. Wang, “Prediction for noisy non-
linear time series by echo state network based on dual estimation,”
Neurocomputing, vol. 82, pp. 186–195, 2012.
49. Z. Zhao, W. Chen, X. Wu, P. C. Chen, and J. Liu, “Lstm network: a
deep learning approach for short-term trafﬁc forecast,” IET Intelligent
Transport Systems, vol. 11, no. 2, pp. 68–75, 2017.
50. S. Brusca, G. Capizzi, G. Lo Sciuto, and G. Susi, “A new design
methodology to predict wind farm energy production by means of a
spiking neural network–based system,” International Journal of Numer-
ical Modelling: Electronic Networks, Devices and Fields, vol. 32, no. 4,
p. e2267, 2019.
51. A. S. Devi, G. Maragatham, K. Boopathi, and A. Rangaraj, “Hourly
day-ahead wind power forecasting with the eemd-cso-lstm-efg deep
learning technique,” Soft Computing, vol. 24, no. 16, pp. 12391–12411,
52. P. Lu, L. Ye, W. Zhong, Y. Qu, B. Zhai, Y. Tang, and Y. Zhao, “A novel
spatio-temporal wind power forecasting framework based on multi-
output support vector machine and optimization strategy,” Journal of
Cleaner Production, vol. 254, p. 119993, 2020.
53. R. Chandra, S. Goyal, and R. Gupta, “Evaluation of deep learning
models for multi-step ahead time series prediction,” IEEE Access,
vol. 9, pp. 83105–83123, 2021.
54. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521,
no. 7553, pp. 436–444, 2015.
55. X. Chen, X. Zhang, M. Dong, L. Huang, Y. Guo, and S. He, “Deep
learning-based prediction of wind power for multi-turbines in a wind
farm,” Frontiers in Energy Research, vol. 9, p. 723775, 2021.
56. S. C. Nwokolo, S. O. Amadi, A. U. Obiwulu, J. C. Ogbulezie, and E. E.
Eyibio, “Prediction of global solar radiation potential for sustainable
and cleaner energy generation using improved angstrom-prescott and
gumbel probabilistic models,” Cleaner Engineering and Technology,
vol. 6, p. 100416, 2022.
57. S. C. Nwokolo, A. U. Obiwulu, J. C. Ogbulezie, and S. O. Amadi,
“Hybridization of statistical machine learning and numerical models for
improving beam, diffuse and global solar radiation prediction,” Cleaner
Engineering and Technology, vol. 9, p. 100529, 2022.
58. A. Sagheer and M. Kotb, “Unsupervised pre-training of a deep lstm-
based stacked autoencoder for multivariate time series forecasting
problems,” Scientiﬁc reports, vol. 9, no. 1, p. 19038, 2019.
59. W. Bao, J. Yue, and Y. Rao, “A deep learning framework for ﬁnancial
time series using stacked autoencoders and long-short term memory,”
PloS one, vol. 12, no. 7, p. e0180944, 2017.
60. N. Shabbir, L. Kütt, M. Jawad, O. Husev, A. U. Rehman, A. A. Gardezi,
M. Shaﬁq, and J.-G. Choi, “Short-term wind energy forecasting us-
ing deep learning-based predictive analytics,” Comput. Mater. Contin,
vol. 72, pp. 1017–1033, 2022.
61. C. Huang, H. R. Karimi, P. Mei, D. Yang, and Q. Shi, “Evolving long
short-term memory neural network for wind speed forecasting,” Infor-
mation Sciences, vol. 632, pp. 390–410, 2023.
62. U. Cali and V. Sharma, “Short-term wind power forecasting using long-
short term memory based recurrent neural network model and variable
selection,” Int. J. Smart Grid Clean Energy, vol. 8, no. 2, pp. 103–110,
63. T. Anushalini and B. Sri Revathi, “Role of machine learning algorithms
for wind power generation prediction in renewable energy management,”
IETE Journal of Research, pp. 1–14, 2023.
64. C. Fu, G.-Q. Li, K.-P. Lin, and H.-J. Zhang, “Short-term wind power
prediction based on improved chicken algorithm optimization support
vector machine,” Sustainability, vol. 11, no. 2, p. 512, 2019.
65. J. Wang, H. Zhu, Y. Zhang, F. Cheng, and C. Zhou, “A novel prediction
model for wind power based on improved long short-term memory
neural network,” Energy, vol. 265, p. 126283, 2023.
66. D. Zhang, X. Peng, K. Pan, and Y. Liu, “A novel wind speed forecasting
based on hybrid decomposition and online sequential outlier robust ex-
treme learning machine,” Energy conversion and management, vol. 180,
pp. 338–357, 2019.
67. A. Kumar and A. S. Ali, “Prospects of wind energy production in the
western ﬁji—an empirical study using machine learning forecasting
algorithms,” in 2017 Australasian Universities Power Engineering Con-
ference (AUPEC), pp. 1–5, IEEE, 2017.
68. S. Sun, Y. Liu, Q. Li, T. Wang, and F. Chu, “Short-term multi-step
wind power forecasting based on spatio-temporal correlations and
transformer neural networks,” Energy Conversion and Management,
vol. 283, p. 116916, 2023.
69. H. Demolli, A. S. Dokuz, A. Ecemis, and M. Gokcek, “Wind power
forecasting based on daily wind speed data using machine learning
algorithms,” Energy Conversion and Management, vol. 198, p. 111823,
70. “Anholt and westermost rough lidar data documentation,”
71. S. Ong and N. Clark, “Commercial and residential hourly load proﬁles
for all tmy3 locations in the united states,” tech. rep., DOE Open Energy
Data Initiative (OEDI); National Renewable Energy Lab.(NREL . . . ,