Megha Sharma, Namita Mittal, Anukram Mishra, and Arun Gupta
[1] W. Cai, R. Mohammaditab, G. Fathi, K. Wakil, A.G. Ebadi,and N. Ghadimi, Optimal bidding and offering strategies ofcompressed air energy storage: A hybrid robust-stochasticapproach, Renewable Energy, 143, 2019, 1–8. [2] Central Electricity Authority, Ministry of Power, Governmentof India, CEA Annual Report 2021-22, CEA, New Delhi, 2020.https://cea.nic.in/annual-report/?lang=en [3] M. Sharma, N. Mittal, A. Mishra, and A. Gupta, Machinelearning-based electricity load forecast for the agriculturesector, International Journal of Software Innovation, 11(1),2023, 1–21. [4] K.M. Powell, A. Sriprasad, W.J. Cole, and T.F. Edgar, Heating,cooling, and electrical load forecasting for a large-scale districtenergy system, Energy, 74, 2014, 877–885. [5] A. Domijan, A. Islam, M. Islam, A.M. Bonet, A. Omole, andH.A. Lladro, Price responsive customer screening using loadcurve with inverted price tier, International Journal of Powerand Energy Systems, 31(1), 2011, 67. [6] H. Kaur and S. Ahuja, “Time series analysis and prediction ofelectricity consumption of health care institution using ARIMAmodel,” Proc. 6th International Conf. on Soft Computingfor Problem Solving (SocProS), vol. 2. Singapore, 2017,pp. 347–358. [7] F. Mirzapour, M. Lakzaei, G. Varamini, M. Teimourian, andN. Ghadimi, A new prediction model of battery and wind-solar output in hybrid power system, Journal of AmbientIntelligence and Humanized Computing, 10(1), 2019, 77–87. [8] M. Mir, M. Shafieezadeh, M.A. Heidari, and N. Ghadimi,Application of hybrid forecast engine based intelligentalgorithm and feature selection for wind signal prediction,Evolving Systems, 11(4), 2020, 559–573. [9] N. Ghadimi, A. Akbarimajd, H. Shayeghi, and O. Abedinia,Two stage forecast engine with feature selection techniqueand improved meta-heuristic algorithm for electricity loadforecasting, Energy, 161, 2018, 130–142. [10] S.S. Subbiah and J. Chinnappan, An improved short termload forecasting with ranker based feature selection technique,Journal of Intelligent & Fuzzy Systems: Applications inEngineering and Technology, 39(5), 2020, 6783–6800. [11] A.M. Pirbazari, A. Chakravorty, and C. Rong, Evaluatingfeature selection methods for short-term load forecasting,Proc. IEEE Int. Conf. on Big Data and Smart Computing(BigComp), Kyoto, 2019, 1–8. [12] A.R. Gollou and N. Ghadimi, A new feature selection andhybrid forecast engine for day-ahead price forecasting ofelectricity markets, Journal of Intelligent & Fuzzy Systems,32(6), 2017, 4031–4045. [13] Y. Wang, S. Sun, X. Chen, X. Zeng, Y. Kong, J. Chen, Y. Guo,and T. Wang, Short-term load forecasting of industrial cus-tomers based on SVMD and XGBoost, International Journalof Electrical Power & Energy Systems, 129, 2021, 106830. [14] X. Deng, A. Ye, J. Zhong, D. Xu, W. Yang, Z. Song, Z. Zhang,J. Guo, T. Wang, Y. Tian, H. Pan, Z. Zhang, H. Wang, C.Wu, J. Shao, and X. Chen, Bagging–XGBoost algorithm basedextreme weather identification and short-term load forecastingmodel, Energy Reports, 8, 2022, 8661–8674. [15] S. Bouktif, A. Fiaz, A. Ouni, and M.A. Serhani, Optimal deeplearning LSTM model for electric load forecasting using featureselection and genetic algorithm: Comparison with machinelearning approaches, Energies, 11(7), 2018, 1636. [16] S. Saravanan and K. Karunanithi, Forecasting of electricenergy consumption in agriculture sector of India usingANN technique, International Journal of Pure and AppliedMathematics, 119(10), 2018, 261–271. [17] M. Sharma, N. Mittal, A. Mishra, and A. Gupta, “Analyticalmachine learning for medium-term load forecasting towardsagricultural sector,” Proc. 2nd Doctoral Symposium on Com-putational Intelligence (DoSCI), Singapore, 2021, pp. 581–592. [18] JVVNL, Jaipur Vidyut Vitaran Nigam Ltd., Departmentof Energy, Government of Rajasthan, 2020. https://energy.rajasthan.gov.in/content/raj/energy-department/jaipur-vidyut-vitran-nigam-ltd-/en/home.html# [19] F. Mart´ınez-´Alvarez, A. Troncoso, G. Asencio-Cort´es, andJ.C. Riquelme, A survey on data mining techniques appliedto electricity-related time series forecasting, Energies, 8(11),2015, 13162–13193. [20] G.E.P. Box, G.M. Jenkins, G.C. Reinsel, and G.M. Ljung,Time series analysis: Forecasting and control. (Hoboken, NJ:Wiley, 2015). [21] M. Cai, M. Pipattanasomporn, and S. Rahman, Day-aheadbuilding-level load forecasts using deep learning vs. traditionaltime-series techniques, Applied Energy, 236, 2019, 1078–1088. [22] J.W. Taylor, Short-term electricity demand forecasting usingdouble seasonal exponential smoothing, Journal of theOperational Research Society, 54(8), 2003, 799–805. [23] R. Banik, P. Das, S. Ray, and A. Biswas, Prediction of electricalenergy consumption based on machine learning technique,Electrical Engineering, 103(2), 2021, 909–920.
Important Links:
Go Back