[1] HajiMirzaei, S. M. A., Behrouzifar, M., Bahadori S., Malek Hosseini, A., "Evaluating approved policies in the field of energy and presenting proposed policies to improve energy governance in Iran", Quarterly Energy Economics Review, Vol. 20, pp. 269-305, 2024 (in persian), http://iiesj.ir/article-1-1617-en.html
[2] Hafezi, R., Akhavan, A., Pakseresht, S., "Development a hybrid computational model based on Artificial Neural Network to predict natural gas Global Demand", Energy Engineering and Management, Vol. 8, Issue 4, pp. 38-49, 2019 (in persian), https://doi.org/10.22052/8.4.38.
[3] Sepehrifar, H., Lotfizade, F., Farahmandian, A., "Analysis of main factors of resedential gas consumption in Iran", Quarterly Journal of Energy Policy and Planning Research, Vol. 7, No. 4, pp. 1-27, 2022 (in persian), http://epprjournal.ir/article-1-1077-en.html.
[4] Daghbandan, A., Setayesh, N., "Modeling and prediction of natural gas consumption with help of multi objective GMDH-Type Neural Network. Case study: regional gas distribution company of Rasht city", Journal of Energy, Vol. 19, No. 4, 2017 (in persian), http://necjournals.ir/article-1-831-en.html.
[5] Baradaran, V., Shafiei, S., "Development of Markov Chain Grey Regression Model to Forecast the Annual Natural Gas Consumption", Quarterly Journal of Energy Policy and Planning Research, Vol. 15, No. 63, pp. 243-267, 2020 (in persian), http://iiesj.ir/article-1-1114-en.html .
[6] Azami, S., "The relationship between natural gas consumption and economic growth given structural breaks in countries exporting natural gas: an approach using Dumitrescu-Hurlin", Quarterly Journal of Energy Policy and Planning Research, Vol. 14, No. 57, pp. 39-64, 2018, (in persian), http://iiesj.ir/article-1-968-en.html.
[7] Karimi, T., Sadeghi, Moghaddam, M. R., Rahnama, R., "Investigating the effect of temperature changes on natural gas consumption in Iran", Energy Economics Studies, Vol. 24, No. 6, pp. 193-218, 2010 (in persian), https://ensani.ir/fa/article/70509.
[8] Esmaeili, S., "Prediction of natural gas consumption in subscribers of South Khorasan province (Birjand) using deep learning tools", M.S Thesis, Islamic Azad University, Birjand Branch, 2011 (in persian).
[9] Sun, F., Ren, L., Jin, W., "
FastNet: A feature aggregation spatiotemporal network for predictive learning", Engineering Applications of Artificial Intelligence, Vol. 130, p. 107785, 2024,
https://doi.org/10.1016/j.engappai.2023.107785.
[10] Wu, X., Zhan, J., Ding, W., "
TWC-EL: A multivariate prediction model by the fusion of three-way clustering and ensemble learning",
Information Fusion, Vol. 100, p. 101966, 2023,
https://doi.org/10.1016/j.inffus.2023.101966.
[11] Ramzan, M., Abbasi, K. R., Iqbal, H. A., Adebayo, T. S., "
What’s at Stake? The empirical importance of government revenue and debt and renewable energy for environmental neutrality in the US economy", Renewable Energy, Vol. 205, pp. 475–489, Mar. 2023,
https://doi.org/10.1016/j.renene.2023.01.071.
[12] Su, H., Zio, E., Zhang, J., Xu, M., Li X., Zhang, Z., "
A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model", Energy, Vol. 178, pp. 585–597, 2019,
https://doi.org/10.1016/j.energy.2019.04.167.
[13] Hribar, R., Potočnik, P., Šilc, J., Papa, G., "
A comparison of models for forecasting the residential natural gas demand of an urban area", Energy, Vol. 167, pp. 511–522, 2019,
https://doi.org/10.1016/j.energy.2018.10.175.
[16] Singh, S., Bansal, P., Hosen, M., Bansal, S. K., "
Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM", Resources Policy, Vol. 80, p. 103159, 2023,
https://doi.org/10.1016/j.resourpol.2022.103159.
[17] Li, N., Wang, J., Wu, L., Bentley, Y., "
Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization", Energy, Vol. 215, p. 119118, 2021,
https://doi.org/10.1016/j.energy.2020.119118.
[18] Honari, E., Yaghini, M., Nadimi, M. H., "
Forecasting the natural gas consumption in residential and commercial sectors of Isfahan for five years using Artificial Neural Networks", Research in Production and Operations Management, Vol. 7, No. 1, pp. 247-262, 2016,
https://doi.org/10.22108/jpom.2016.20922.
[19] Doust Mohammadi, A., Sharifi Rad, R., Akbarpour, M., Akarpour, S., "Prediction of natural gas consumption of household customers using data mining procedures, case study: Kerman Gas Company", The First National Conference on Modern and Smart Business Data Mining and Image Processing, 2018 (in persian).
[21] Lu, Z., Pu, H., Wang, F., Hu, Z., Wang, L. "
The expressive power of neural networks: A view from the width", Advances in Neural Information Processing Systems (NeurIPS), 30, 2017,
https://doi.org/10.1145/3326365.3326379.
[22] Goodfellow, I., Bengio, Y., Courville, A. "Deep learning", MIT press, 2016.
[23] Bengio, Y., Delalleau, O., Le, Roux N., "
The curse of highly variable functions for local kernel machines", in Proceedings of the 18th International Conference on Neural Information Processing Systems, in NIPS’05. Cambridge, MA, USA: MIT Press, pp. 107–114, 2005,
https://doi.org/10.1016/j.jmlr.2005.10.001.
[24] Goodfellow, I. J., Le, Q., Saxe, A. M., Lee, H., Ng, A. Y., "
Measuring invariances in Deep Networks", in Proceedings of the 22Nd International Conference on Neural Information Processing Systems, in NIPS’09. USA: Curran Associates Inc., pp. 646–654, 2009,
https://doi.org/10.5555/2999792.2999851.
[25] Bengio, Y., Courville, A., Vincent, P., "
Representation learning: a review and new perspectives", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 8, pp. 1798-1828, 2013,
https://doi.org/10.1109/TPAMI.2013.50 .
[27] Demir, V., Yaseen, Z. M., "
Neurocomputing intelligence models for lakes water level forecasting: a comprehensive review", Neural Computing and Applications
, Vol. 35, pp. 303-343,
https://doi.org/10.1007/s00521-022-07699-z.
[28] Ruma, J. F., Adnan, M. S. G., Dewan, A., Rahman, R. M., "
Particle swarm optimization based LSTM networks for water level forecasting: A case study on Bangladesh river network", Results in Engineering, Vol. 17, p. 100951, 2023,
https://doi.org/10.1016/j.rineng.2023.100951.
)