Development progress of power prediction robot and platform: Its world level very long term prototyping example

Abstract

Global Power Prediction Systems prototype version 2021 is presented with its system decomposition, scope, geographical/administrative/power grid decompositions, and similar. “Welcome”, “sign-up”, “log-in”, and “non-registered user main” web-interfaces are designed as draft on Quant UX. Map canvas is given as world political map with/without world power grid layers on QGIS 3.16.7-Hannover. Data input file is prepared based on several sources (1971-2018). It includes minimum and maximum values due to source value differences. 70/30 principle is applied for train/test splitting (training/testing sets: 1971-2003/2004-2018). 10 models are prepared on R version 4.1.1 with RStudio 2021.09.0+351. These are R::base(lm), R::base(glm), R::tidymodels::parsnip(engine("lm")), R::tidymodels::parsnip(engine("glmnet")) with lasso regularization, R::tidymodels::parsnip(engine("glmnet")) with ridge regularization, R::forecast(auto.arima) auto autoregressive integrated moving average (ARIMA), R::forecast(arima) ARIMA(1,1,2), and ARIMA(1,1,8). Electricity demand in kilowatt-hours at the World level zone for up to 500-years (2019-2519) prediction period with only 1-year interval is forecasted. The best model is the auto ARIMA (mean absolute percentage error MAPE and symmetric mean absolute percentage error SMAPE for minimum and maximum electricity consumption respectively 1,1652; 6,6471; 1,1622; 6,9043). Ex-post and ex-ante plots with 80%-95% confidence intervals are prepared in R::tidyverse::ggplot2. There are 3 alternative scripts (long, short, RStudio Cloud). Their respective runtimes are 41,45; 25,44; and 43,33 seconds. Ex-ante 500-year period (2019-2519) is indicative and informative.

Keywords

• Global power prediction system
• Platform
• Power
• Prototyping
• Robot

References

1. [1] Tercan, E, Eymen, A, Urfali, T, Saracoglu, BO. A sustainable framework for spatial planning of photovoltaic solar farms using GIS and multi-criteria assessment approach in Central Anatolia, Turkey. Land Use Policy 2021; 102:105272, 1-14. DOI: 10.1016/j.landusepol.2020.105272
2. [2] Tercan, E, Saracoglu, BO, Bilgilioglu, SS, Eymen, A, Tapkin, S. Geographic information system based investment system for photovoltaic power plants location analysis in Turkey. Environmental Monitoring and Assessment 2020; 192(297): 1-26. DOI: 10.1007/s10661-020-08267-5
3. [3] Saracoglu, BO. Location selection factors of concentrated solar power plant investments. Sustainable Energy, Grids and Networks 2020; 22(100319): 1-20. DOI: 10.1016/j.segan.2020.100319
4. [4] Solangi, YA, Shah, SAA, Zameer, H, Ikram, M, Saracoglu, BO. Assessing the solar PV power project site selection in Pakistan: based on AHP-fuzzy VIKOR approach. Environmental Science and Pollution Research 2019; 26(29): 30286-30302. DOI: 10.1007/s11356-019-06172-0
5. [5] Saracoglu, BO. An Experimental Fuzzy Expert System Based Application For Go/No-Go Decisions To Geospatial Investigation Studies Of The Regions On Very Large Concentrated Solar Power Plants In European Super-Grid Concept. International Journal of Multidisciplinary Studies and Innovative Technologies 2018; 2(2): 1-6.
6. [6] Saracoglu, BO, de Simon Martin M. Initialization of a Multiobjective Evolutionary Algorithms Knowledge Acquisition System for Renewable Energy Power Plants. Journal of Applied Research on Industrial Engineering 2018; 5(3): 185-204. DOI: 10.22105/jarie.2018.144919.1052.
7. [7] King, A, Saracoglu, BO. Greenough River Solar Farm case study & validation initialization. International Journal of Energy Applications and Technologies 2018; 5(2): 82-97. DOI: 10.31593/ijeat.420701
8. [8] Saracoglu, BO, Ohunakin, OS, Adelekan, DS, Gill, J, Atiba, OE, Okokpujie, IP, Atayero, AA. A Framework for Selecting the Location of Very Large Photovoltaic Solar Power Plants on a Global/Super Grid. Energy Reports 2018; 4: 586-602, DOI: 10.1016/j.egyr.2018.09.002

9. [9] Ohunakin, OS, Saracoglu, BO. A Comparative Study of Selected Multi-Criteria Decision Making Methodologies for Location Selection of Very Large Concentrated Solar Power Plants in Nigeria. African Journal of Science, Technology, Innovation and Development 2018; 10(5): 551-567. DOI: 10.1080/20421338.2018.1495305
10. [10] Saracoglu, BO. Experimental FWA & FOWA Aggregated VLCSPPs' LUR Estimation For GIS Based VEED. International Journal Of Engineering Technologies 2018; 4(2): 70-79.
11. [11] Saracoglu, BO. Solar star projects SAM version 2017.9.5 PVWatts version 5 model case study & validation. International Journal of Energy Applications and Technologies 2018; 5(1): 13-28.
12. [12] Saracoglu, BO. An Experimental Fuzzy Inference System for the Third Core Module of the First Console on the Global Grid Peak Power Prediction System & Its Forecasting Accuracy Measures' Comparisons with the First and the Second Core Modules. Journal Of Energy Systems 2017; 1(2): 75-101.
13. [13] Saracoglu, BO. SEGS VI & Topaz Solar Farm SAM Empirical Trough & PVWatts Models Case Studies & Validation. International Journal of Research In Advanced Engineering Technologies 2017; 1(1): 28-41.
14. [14] Saracoglu, BO. Comparative Study On Experimental Type 1 & Interval & General Type 2 Mamdani FIS for G2P3S. Global Journal of Researches in Engineering: J General Engineering 2017; 17(2): 27-42.
15. [15] Saracoglu, BO. Comparative Study On Experimental 2 to 9 Triangular Fuzzy Membership Function Partitioned Type 1 Mamdani's FIS For G2EDPS. Global Journal of Researches in Engineering: J General Engineering 2017; 17(2): 1-18.
16. [16] Saracoglu, BO. G2EDPS's First Module & Its First Extension Modules. American Journal of Applied Scientific Research 2017; 3(4): 33-48. DOI: 10.11648/j.ajasr.20170304.13
17. [17] Saracoglu, BO. Long Term Electricity Demand & Peak Power Load Forecasting Variables Identification & Selection. Science Journal of Circuits, Systems and Signal Processing 2017; 6(2): 18-28. DOI: 10.11648/j.cssp.20170602.13
18. [18] Saracoglu, BO. Location Selection Factors of Small Hydropower Plant Investments Powered By SAW, Grey WPM & Fuzzy DEMATEL Based On Human Natural Language Perception. International Journal of Renewable Energy Technology 2017; 8(1): 1-23. DOI: 10.1504/IJRET.2017.080867
19. [19] Saracoglu, BO. A PROMETHEE I, II and GAIA based approach by Saaty’s subjective criteria weighting for small hydropower plant investments in Turkey. International Journal of Renewable Energy Technology 2016; 7(2): 163-183. DOI: 10.1504/IJRET.2016.076094
20. [20] Saracoglu, BO. A Qualitative Multi-Attribute Model for the Selection of the Private Hydropower Plant Investments in Turkey: By Foundation of the Search Results Clustering Engine (Carrot2), Hydropower Plant Clustering, DEXi and DEXiTree. Journal of Industrial Engineering and Management 2016; 9(1): 152-178. DOI: 10.3926/jiem.1142
21. [21] Saracoglu, BO. A Comparative Study Of AHP, ELECTRE III & ELECTRE IV By Equal Objective & Shannon’s Entropy Objective & Saaty’s Subjective Criteria Weighting On The Private Small Hydropower Plants Investment Selection Problem In Turkey. International Journal of the Analytic Hierarchy Process 2015; 7(3): 470-512. DOI: 10.13033/ijahp.v7i3.343
22. [22] Saracoglu, BO. An Experimental Research of Small Hydropower Plant Investments Selection in Turkey by Carrot2, DEXi, DEXiTree. Journal of Investment and Management 2015; 4(1): 47-60. DOI: 10.11648/j.jim.20150401.17
23. [23] Saracoglu, BO. An AHP Application In The Investment Selection Problem Of Small Hydropower Plants In Turkey. International Journal of the Analytic Hierarchy Process 2015; 7(2): 211-239. DOI: 10.13033/ijahp.v7i2.198
24. [24] Saracoglu, BO. An Experimental Research Study On The Solution Of A Private Small Hydropower Plant Investments Selection Problem By ELECTRE III/IV, Shannon’s Entropy & Saaty’s Subjective Criteria Weighting. Advances in Decision Sciences 2015; 548460. DOI: 10.1155/2015/548460
25. [25] Saracoglu, BO, Holecek, P, Pavlacka, O. Robot For Parabolic Trough Concentrated Solar Power Plants. ResearchGate 2020; Preprint. DOI: 10.13140/RG.2.2.26478.43840
26. [26] Saracoglu, BO. SEGS NREL SAM Version 2018.11.11 Case Study & Validation. ResearchGate 2018; Preprint. DOI: 10.13140/RG.2.2.31845.86245
27. [27] Tercan, E, Dereli, M.A, Saracoglu, BO. Location alternatives generation and elimination of floatovoltaics with virtual power plant designs. Renewable Energy 2022; 193: 1150-1163. DOI: 10.1016/j.renene.2022.04.145
28. [28] Saracoglu, BO. An Experimental Study on Fuzzy Expert System: Proposal for Financial Suitability Evaluation of Commercial and Participation Banks in Power Plant Projects in Turkey. In: WSC18 2014. Proceedings of the 18th Online World Conference on Soft-Computing in Industrial Applications. Advances in Intelligent Systems and Computing; 1-12 December 2014: Springer Cham (2019), 864, pp. 81-91. DOI: 10.1007/978-3-030-00612-9_8
29. [29] Saracoglu, BO. An Experimental Case Study on Fuzzy Logic Modeling for Selection Classification of Private Mini Hydropower Plant Investments in the Very Early Investment Stages in Turkey. In: WSC18 2014. Proceedings of the 18th Online World Conference on Soft-Computing in Industrial Applications. Advances in Intelligent Systems and Computing; 1-12 December 2014: Springer Cham (2019), 864, pp. 69-80. DOI: 10.1007/978-3-030-00612-9_7
30. [30] Saracoglu, BO. Experimental FOWA Aggregated Location Selection Model for VLCPVPPs in MENA Region in the Very Early Engineering Design. In: WSC18 2014. Proceedings of the 18th Online World Conference on Soft-Computing in Industrial Applications. Advances in Intelligent Systems and Computing; 1-12 December 2014: Springer Cham (2019), 864, pp. 36-45. DOI: 10.1007/978-3-030-00612-9_4
31. [31] Saracoglu, BO. Experimental FuzzyWA Aggregated Location Selection Model for Very Large Photovoltaic Power Plants in Global Grid in the Very Early Engineering Design Process Stage. In: WSC18 2014. Proceedings of the 18th Online World Conference on Soft-Computing in Industrial Applications. Advances in Intelligent Systems and Computing; 1-12 December 2014: Springer Cham (2019), 864, pp. 25-35. DOI: 10.1007/978-3-030-00612-9_3
32. [32] Saracoglu, BO. Multiobjective Evolutionary Algorithms Knowledge Acquisition System for Renewable Energy Power Plants. Budapest, Hungary: MedCrave Group LLC, 2019.
33. [33] Saracoglu, BO. Analytic Network Process vs. Benjamin Franklin’s Rule To Select Private Small Hydropower Plants Investments. Budapest, Hungary: MedCrave Group LLC, 2018.
34. [34] Li, Y, Saracoglu, BO. Location and Investment Factors of Hydropower Plants. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 2021; 1-19. DOI: 10.1080/15567036.2021.1963015
35. [35] Chung, WJ, Wang, Z, Liu, AY, Hodel, JM. Systems And Methods For Predicting Video Quality Based On Objectives Of Video Producer. US2021027065A1, United States Patent and Trademark Office, 28 January 2021.
36. [36] Mckenzie, D, Borum, P, Borum, M, Williams, B. Automated Media Campaign Management System. US2016189198A1, United States Patent and Trademark Office, 30 January 2016.
37. [37] Howorka, ER. Automated Trading Systems. US2010114755A1, United States Patent and Trademark Office, 6 May 2010.
38. [38] Mittal, H, Sugden, E. Algorithmic trading portal and method. US2007250436A1, United States Patent and Trademark Office, 25 October 2007.
39. [39] Glodjo, A, Bronson, ND, Harrington, SE. Global Trading Network. US2006195386A1, United States Patent and Trademark Office, 17 August 2006.
40. [40] Takahashi, H. Fuzzy Control System For Automatic Transmission. US4841815, United States Patent and Trademark Office, 27 June 1989.
41. [41] Sakai, I, Iwaki, Y, Haga, T, Sakaguchi, S, Suzaki, Y, all of Saitama Japan. Vehicle Automatic Transmission Control System Using Fuzzy Logic To Determine Slope And An Inferred Driver's Intention To Decelerate (DEC) To Determine The Correct Gear Position. US5389050A, United States Patent and Trademark Office, 14 February 1995.
42. [42] Rubin, SH. Geodesic Search And Retrieval System And Method Of Semi-Structured Databases. US8155949B1, United States Patent and Trademark Office, 10 April 2012.
43. [43] Benureau, FCY, Rougier, NP. Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions. Front Neuroinform 2018; 11(69): 1-8. DOI: 10.3389/fninf.2017.00069
44. [44] Pranadi, AD, Setiawan, EA. Cost benefit analysis for peer-to-peer mechanism in residential sector of a single buyer electricity market. Journal of Energy Systems 2020; 4(4): 179-195. DOI: 10.30521/jes.748138
45. [45] Savage, WE, Olejniczak, AJ. Do senior faculty members produce fewer research publications than their younger colleagues? Evidence from Ph.D. granting institutions in the United States. Scientometrics 2021; 126: 4659–4686. DOI: 10.1007/s11192-021-03957-4
46. [46] Kiesslich, T, Beyreis, M, Zimmermann, G, Traweger, A. Citation inequality and the Journal Impact Factor: median, mean, (does it) matter? Scientometrics 2021; 126: 1249–1269. DOI: doi.org/10.1007/s11192-020-03812-y
47. [47] McMahan, P, McFarland, DA. Creative Destruction: The Structural Consequences of Scientific Curation American Sociological Review 2021; 86(2): 341–376. DOI: 10.1177/0003122421996323
48. [48] Boudreault, I. Automatic Article Generator from Extracted Databases. Projektarbeten 2005; 23.
49. [49] Abd-Elaal, ES, Gamage, SH, Mills, JE. Artificial intelligence is a tool for cheating academic integrity. In: AAEE 2019. Proceedings of the 30th Annual Conference for the Australasian Association for Engineering Education. Educators Becoming Agents of Change: Innovate, Integrate, Motivate; Engineers Australia (2019), pp. 397
50. [50] Trines, S, Academic Fraud, Corruption, and Implications for Credential Assessment World Education News and Reviews 2017
51. [51] SCIgen - An Automatic CS Paper Generator. (Accessed on 2021, August 09)
52. [52] Azadeh, A, Saberi, M, Nadimi, V, Iman, M, Behrooznia, A. An integrated intelligent neuro-fuzzy algorithm for long-term electricity consumption: cases of selected EU countries. Acta Polytechnica Hungarica 2010; 7(4): 71-90.
53. [53] Dilaver, Z, Hunt, LC. Modelling and forecasting Turkish residential electricity demand. Energy Policy 2011; 39(6): 3117-3127. DOI: 10.1016/j.enpol.2011.02.059
54. [54] Ghanbari, A, Kazemi, SM, Mehmanpazir, F, Nakhostin, MM. A cooperative ant colony optimization-genetic algorithm approach for construction of energy demand forecasting knowledge-based expert systems. Knowledge-Based Systems 2013; 39: 194-206. DOI: 10.1016/j.knosys.2012.10.017
55. [55] Karabulut, K, Alkan, A, Yilmaz, AS. Long term energy consumption forecasting using genetic programming. Mathematical and Computational Applications 2008; 13(2): 71-80. DOI: 10.3390/mca13020071
56. [56] Lee, YS, Tong, LI. Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy conversion and Management 2011; 52(1): 147-152. DOI: 10.1016/j.enconman.2010.06.053
57. [57] Hamzacebi, C, Es, HA, Cakmak, R. Forecasting of Turkey’s monthly electricity demand by seasonal artificial neural network. Neural Computing and Applications 2019; 31(7): 2217-2231. DOI: 10.1007/s00521-017-3183-5
58. [58] Hong, WC. Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Conversion and Management 2009; 50(1): 105-117. DOI: 10.1016/j.enconman.2008.08.031
59. [59] Hyndman RJ, Fan S. Density forecasting for long-term peak electricity demand. IEEE Transactions on Power Systems 2009; 25(2): 1142-1153. DOI: 10.1109/TPWRS.2009.2036017
60. [60] Sorjamaa, A. Hao, J. Reyhani, N. Ji, Y. Lendasse, A. Methodology for long-term prediction of time series. Neurocomputing. 2007; 70(16-18): 2861-2869. DOI: 10.1016/j.neucom.2006.06.015
61. [61] Akarsu, G. Forecasting Regional Electricity Demand for Turkey. International Journal of Energy Economics and Policy 2017; 7(4): 275-28.
62. [62] Abiyev, RH. Fuzzy wavelet neural network for prediction of electricity consumption. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 2009; 23(2): 109-118. DOI: 10.1017/S0890060409000018
63. [63] Azadeh, A, Saberi, M, Gitiforouz, A, Saberi, Z. A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation. Expert Systems with Applications 2009; 36(8): 11108-11117. DOI: 10.1016/j.eswa.2009.02.081
64. [64] Azadeh, A, Saberi, M, Gitiforouz, A. An integrated simulation-based fuzzy regression-time series algorithm for electricity consumption estimation with non-stationary data. Journal of the Chinese Institute of Engineers 2011; 34(8): 1047-1066. DOI: 10.1080/02533839.2011.576502
65. [65] Erol, AH, Nilay, O, Asim, T, Ozel, S, Zaim, S, Demirel, OF. Forecasting electricity consumption of Turkey using time series methods and neural networks. In: ICM 2012 Proceedings of the International Conference in Mathematics; 11-14 March 2012: United Arab Emirates University, Al Ain, pp.117-127.
66. [66] Iranmanesh, H, Abdollahzade, M, Miranian, A. Mid-term energy demand forecasting by hybrid neuro-fuzzy models. Energies 2012; 5(1): 1-21. DOI: 10.3390/en5010001
67. [67] Bunnoon, P, Chalermyanont, K, Limsakul, C. Mid Term Load Forecasting of the Country Using Statistical Methodology: Case study in Thailand. In: IEEE 2009 International Conference on Signal Processing Systems; 15-17 May 2009: International Association of Computer Science and Information Technology, Singapore, pp.924-928. DOI: 10.1109/ICSPS.2009.174
68. [68] Hassan, S, Khosravi, A, Jaafar, J. Examining performance of aggregation algorithms for neural network-based electricity demand forecasting. Electrical Power and Energy Systems 2015; 64: 1098–1105. DOI: 10.1016/j.ijepes.2014.08.025
69. [69] Marwala, L, Twala, B. Forecasting electricity consumption in South Africa: ARMA, Neural networks and Neuro-fuzzy systems, 2014.
70. [70] Moreno-Chaparro, C, Salcedo-Lagos, J, Rivas, E, Canon, AO. A Method for the Monthly Electricity Demand Forecasting in Colombia based on Wavelet Analysis and a Nonlinear Autoregressive Model. Ingenieria 2011; 16(2): 94–106.
71. [71] Xiong, T, Bao, Y, Hu, Z. Interval Forecasting of Electricity Demand: A Novel Bivariate EMD-based Support Vector Regression Modeling Framework. International Journal of Electrical Power and Energy Systems 2014; 63: 353-362. DOI: 10.1016/j.ijepes.2014.06.010
72. [72] Elamin, N, Fukushige, M. Modeling and forecasting hourly electricity demand by SARIMAX with interactions. Energy 2018; 165: 257-268.
73. [73] Jun, D, Pei, W, Xihao, D. Short-term power load forecasting based on EMD-grey model. American Journal of Electrical Power and Energy Systems 2018; 7(4): 42-49. DOI: 10.11648/j.epes.20180704.11
74. [74] Barzamini, R, Menhaj, MB, Khosravi, A, Kamalvand, SH. Short Term Load Forecasting for Bakhtar Region Electric Co. Using Multi Layer Perceptron and Fuzzy Inference systems. 2014, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
75. [75] Mordjaoui, M, Boudjema, B, Bouabaz, M, Daira, R. Short term electric load forecasting using Neuro-fuzzy modeling for nonlinear system identification. 2010. LRPCSI Laboratory, University of 20August, Skikda, 5, 1-6.
76. [76] Jain, A, Srinivas, E, Rauta, R. Short term load forecasting using fuzzy adaptive inference and similarity. In NaBIC 2009 World Congress on Nature & Biologically Inspired Computing; 9-11 December 2009: IEEE, pp.1743-1748.
77. [77] Shayeghi, H, Ghasemi, A. A New Hybrid Algorithm For Short Term Load Forecasting. International Journal on Technical and Physical Problems of Engineering, 2015; 7(1): 38-45.
78. [78] Gabr, ASEM, Hassan, MAM, Abul-Haggag, OY. Artificial Intelligence Based Approach Compared With Stochastic Modelling For Electrical Load Forecasting. In ICREPQ’11 International Conference on Renewable Energies and Power Quality; 13-15 April 2011: European Association for the Development of Renewable Energies, Environment and Power Quality (EA4EPQ).
79. [79] Jain, A, Jain, MB. Fuzzy modeling and similarity based short term load forecasting using swarm intelligence-a step towards smart grid. In BIC-TA 2012, Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications; 2013: Springer, pp. 15-27.
80. [80] Centra, M. Hourly Electricity Load Forecasting: An Empirical Application to the Italian Railways. World Academy of Science, Engineering and Technology 2011; 56: 1026-1033.
81. [81] Duan, P, Xie, K, Guo, T, Huang, X. Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques. Energies 2011; 4: 173-184. DOI: 10.3390/en4010173
82. [82] Sachdeva, S, Singh, M, Singh, UP, Arora, AS. Efficient Load Forecasting Optimized by Fuzzy Programming and OFDM Transmission. Advances in Fuzzy Systems 2011;326763. DOI: 10.1155/2011/326763
83. [83] Cardenas, JJ, Romeral, L, Garcia, A, Andrade ,F. Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side. Expert Systems with Applications 2012; 39(5): 5557-5565. DOI: 10.1016/j.eswa.2011.11.062
84. [84] Deshani, KAD, Hansen, LL, Attygalle, MDT, Karunaratne, A. A Study of the Dynamic Behaviour of Daily Load Curve for Short Term Predictions. In International Symposium for Next Generation Infrastructure; 1-4 October 2013:, Wollongong, Australia.
85. [85] Helmy, T, Al-Jamimi, H, Ahmed, B, Loqman, H. Fuzzy Logic–Based Scheme for Load Balancing in Grid Services. A Journal of Software Engineering and Applications 2012; 5: 149-156. DOI: 10.4236/jsea.2012.512b029
86. [86] Ismail, Z, Mansor, R. Fuzzy logic approach for forecasting half-hourly Malaysia electricity load demand. Int inst forecast 2011; 1-17.
87. [87] Liu, K, Subbarayan, S, Shoults, RR, Manry, MT, Kwan, C, Lewis, FI, Naccarino, J. Comparison of very short-term load forecasting techniques. IEEE Transactions on Power Systems 1996; 11(2): 877-882.
88. [88] Moraes, LA, Flauzino, RA, Araújo, MA, Batista, OE. A fuzzy methodology to improve time series forecast of power demand in distribution systems. In 2013 IEEE Power & Energy Society General Meeting; 26-29 July 2013: IEEE, pp. 1-5.
89. [89] Hu, Z, Bao, Y, Xiong, T. Electricity load forecasting using support vector regression with memetic algorithms. The Scientific World Journal 2013; 2013: 292575. DOI: 10.1155/2013/292575.
90. [90] Mohamed, N, Ahmad, MH, Suhartono, Ahmad, WMAW. Forecasting Short Term Load Demand Using Multilayer Feed-forward (MLFF) Neural Network Model. Applied Mathematical Sciences 2012; 6(108): 5359-5368.
91. [91] Wang, Y, Zhang, N, Tan, Y, Hong, T, Kirschen, DS, Kang, C. Combining Probabilistic Load Forecasts. IEEE Trans. Smart Grid 2018, arXiv preprint 1803.06730v
92. [92] Esfahani, M. Neuro-fuzzy Approach for Short-term Electricity Price Forecasting Developed MATLAB-based Software, Fuzzy Inf. Eng.
93. [93] Gayretli, G, Yucekaya, A, Humeyra Bilge, A. An analysis of price spikes and deviations in the deregulated Turkish power Market. Energy Strategy Reviews 2019; 100376. DOI: 10.1016/j.esr.2019.100376
94. [94] Gianfreda, A, Ravazzolo, F, Rossini, L. Comparing the forecasting performances of linear models for electricity prices with high RES penetration. International Journal of Forecasting 2020; 36: 974–986. DOI: 10.1016/j.ijforecast.2019.11.002
95. [95] Gupta, A, Chawla, P, Chawla, S. Short Term Electricity Price Forecasting Using ANN and Fuzzy Logic under Deregulated Environment. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 2013; 2(8): 3852–3858.
96. [96] Jun, D, Peiwen, Y. Short-Term Electricity Price Forecasting Based on Grey Prediction GM(1,3) and Wavelet Neural Network. American Journal of Electrical Power and Energy Systems 2018; 7(4): 50-55. DOI: 10.11648/j.epes.20180704.12
97. [97] Ugurlu, U, Oksuz, I, Tas, O. Electricity price forecasting using recurrent neural networks. Energies 2018; 11(5): 1255. DOI: 10.3390/en11051255
98. [98] Zhang, K, Troitzsch, S. Robust Scheduling for Networked Microgrids Under Uncertainty. Frontiers in Energy Research 2021; 9:632852. DOI: 10.3389/fenrg.2021.632852
99. [99] Hassan, S, Khosravi, A, Jaafar, J, Raza, MQ. Electricity load and price forecasting with influential factors in a deregulated power industry. In 2014 SOSE, IEEE 9th International Conference on System of Systems Engineering; 9-13 June 2014: IEEE, pp.79-84.
100. [100] Yang, W, Wang, J, Niu, T, Du, P. A novel system for multi-step electricity price forecasting for electricity market management. Applied Soft Computing Journal 2020; 88: 106029. DOI: 10.1016/j.asoc.2019.106029
101. [101] Wang, F, Li, K, Zhou, L, Ren, H, Contreras, J, Shafie-khah, M, Catalao, JPS. Daily pattern prediction based classification modeling approach for dayahead electricity price forecasting. Electrical Power and Energy Systems 2019; 105: 529–540. DOI: 10.1016/j.ijepes.2018.08.039
102. [102] Arfoa, AA. Long-Term Load Forecasting of Southern Governorates of Jordan Distribution Electric System. Energy and Power Engineering 2015; 7: 242-253. DOI: 10.4236/epe.2015.75023.
103. [103] Goia, A, May, C, Fusai, G. Functional clustering and linear regression for peak load forecasting. International Journal of Forecasting 2010; 26: 700–711. DOI: 10.1016/j.ijforecast.2009.05.015
104. [104] Khuntia, SR, Rueda, JL, van der Meijden MAMM. Forecasting the load of electrical power systems in mid and long-term horizons: A review. IET Generation, Transmission and Distribution, 2016; 10(16): 3971-3977. DOI: 10.1049/iet-gtd.2016.0340
105. [105] McNeil, MA, Karali, N, Letschert, V. Forecasting Indonesia's electricity load through 2030 and peak demand reductions from appliance and lighting efficiency. Energy for Sustainable Development, 2019; 49: 65–77. DOI: 10.1016/j.esd.2019.01.001
106. [106] Ogurlu, H, Cetinkaya, N. Electrical Load Forecasting between 2015 and 2035 for Turkey using Mathematical Modelling and Dynamic Programming, International Journal of Science Technology & Engineering 2016; 2(8): 279-283.
107. [107] AboGaleela, M, El-Marsafawy, M, El-Sobki, M. Optimal Scheme with Load Forecasting for Demand Side Management (DSM) in Residential Areas. Energy and Power Engineering, 2013; 5: 889-896. DOI: 10.4236/epe.2013.54B171
108. [108] Asad, M. Finding the Best ARIMA Model to Forecast Daily Peak Electricity Demand. In: Proceedings of the Fifth Annual Applied Statistics Education and Research Collaboration (ASEARC) Conference – Looking to the future - Programme and Proceedings; 2-3 February 2012: University of Wollongong, Dubai, pp.1-4.
109. [109] Ismail, Z, Yahya, A, Mahpol, KA. Forecasting Peak Load Electricity Demand Using Statistics and Rule Based Approach. American Journal of Applied Sciences 2009; 6(8): 1618-1625.
110. [110] Sonika, D, Darshan, SS, Daljeet, K. Long Term Load Forecasting Using Fuzzy Logic Methodology. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 2015; 4(6): 5578- 5585. DOI: 10.15662/ijareeie.2015.0406047
111. [111] Ozerdem, OC, Olaniyi, EO, Oyedotun, OK, Short term load forecasting using particle swarm optimization neural network, In: ICSCCW 2017, 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception; 24-25 August 2017: Budapest, Hungary, pp.382–393. DOI: 10.1016/j.procs.2017.11.254
112. [112] Alduailij, MA, Petri, I, Rana, O, Alduailij, MA, Aldawood, AS. Forecasting peak energy demand for smart buildings. The Journal of Supercomputing 2020; 77: 6356–6380. DOI: 10.1007/s11227-020-03540-3
113. [113] Ciabattoni, L, Grisostomi, M, Ippoliti, G, Longhi, S. Fuzzy logic home energy consumption modeling for residential photovoltaic plant sizing in the new Italian scenario. Energy 2014; 74: 359-367.
114. [114] Gammon, R, Sallah, M. Preliminary Findings From a Pilot Study of Electric Vehicle Recharging From a Stand-Alone Solar Minigrid. Frontiers Energy Research 2021; 8: 563498. DOI: 10.3389/fenrg.2020.563498
115. [115] Li, H, Wang, Y, Zhang, X, Fu, G. Evaluation Method of Wind Power Consumption Capacity Based on Multi-Fractal Theory. Frontiers Energy Research 2021; 9:634551. DOI: 10.3389/fenrg.2021.634551
116. [116] Mai, T, Ryan, W, Galen, B, Lori, B, Jenny, H, David, K, Venkat, K, Jordan, M, Dev, M. A Prospective Analysis of the Costs, Benefits, and Impacts of U.S. Renewable Portfolio Standards. NREL/TP-6A20-67455/LBNL-1006962. National Renewable Energy Laboratory and Lawrence Berkeley National Laboratory. California, U.S.A. 2016.
117. [117] Weinand, JM, McKenna, R, Kleinebrahm, M, Scheller, F, Fichtner ,W. The impact of public acceptance on cost efficiency and environmental sustainability in decentralized energy systems. Patterns 2021; 2:100301. DOI: 10.1016/j.patter.2021.100301
118. [118] Barasa, M, Bogdanov, D, Oyewo, AS, Breyer, C. A Cost Optimal Resolution for Sub-Saharan Africa powered by 100% Renewables for Year 2030 Assumptions. In: 32nd European Photovoltaic Solar Energy Conference; 20 – 24 June 2016: Munich, Germany, pp.117-127.
119. [119] Bogdanov, D, Toktarova, A, Breyer, C. Transition Towards 100% Renewable Energy system by 2050 for Kazakhstan. In: Astana Economic Forum; 15-16 June 2017: Astana, Kazakhstan.
120. [120] National Renewable Energy Laboratory (NREL). Renewable Electricity Futures Study. Hand, MM, Baldwin, S, DeMeo, E, Reilly, J, Arent, D, Porro, G, Mai, T, Meshek, M, Sandor, D. eds. 4 vols. NREL/TP-6A20-52409. Golden, CO: National Renewable Energy Laboratory. 2012
121. [121] Brinkman, G, Dominique, B, Grant, B, Caroline, D, Paritosh, D, Jonathan, H, Eduardo, I, Ryan, J, Sam, K, Sinnott, M, Vinayak, N, Joshua, N, Avi, P, Michael, R, Ben, S, Gord, S, Jiazi, Z. The North American Renewable Integration Study: A U.S. Perspective. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-79224. 2021
122. [122] Saracoglu, BO. Global Grid Prediction Systems, ResearchGate 2016. DOI: 10.13140/RG.2.1.3575.3040
123. [123] State Council in China, Notice of the State Council on the issuance of a new generation of artificial intelligence development plans; July 20: 2017
124. [124] Office of the President of the Russian Federation, Decree of the President of the Russian Federation on the Development of Artificial Intelligence in the Russian Federation; 10 October 2019.
125. [125] US National Artificial Intelligence Initiative, National Artificial Intelligence Initiative Act of 2020 (NAIIA) 1 January 2021; National Artificial Intelligence Initiative 2017,
126. [126] Haibe-Kains, B, Adam, GA, Hosny, A, Khodakarami, F, MAQC Society Board, Waldron L, Wang B, McIntosh, C, Kundaje, A, Greene, CS, Hoffman, MM, Leek, JT, Huber, W, Brazma, A, Pineau, J, Tibshirani, R, Hastie, T, Ioannidis, JPA, Quackenbush, J, Aerts HJWL. The importance of transparency and reproducibility in artificial intelligence research arXiv:2003.00898 2020
127. [127] Makin, TR, De, Xivry. Ten common statistical mistakes to watch out for when writing or reviewing a manuscript. eLife 2019; 8:e48175. DOI: https://doi.org/10.7554/eLife.48175
128. [128] Taube, A. Observations by a statistical watchdog. Upsala Journal Of Medical Sciences 2021; 126: e5665. DOI: http://dx.doi.org/10.48101/ujms.v126.5665
129. [129] Masic, I, Jankovic, SM, Begic, E. PhD Students and the Most Frequent Mistakes During Data Interpretation by Statistical Analysis Software, Health Informatics Vision: From Data via Information to Knowledge J. Mantas et al. (Eds.) IOS Press 2019. DOI:10.3233/SHTI190028.
130. [130] Asanka, PPGD, Perera, AS. Defining Fuzzy Membership Function Using Box Plot. International Journal Of Research In Computer Applications And Robotics 2017; 5(11): 1-11.
131. [131] Hasan, F, Sobhan, A. Describing Fuzzy Membership Function and Detecting the Outlier by Using Five Number Summary of Data. American Journal of Computational Mathematics 2020; 10: 410-424. DOI: 10.4236/ajcm.2020.103022
132. [132] Wu, D, Mendel, JM. Linguistic Summarization Using IF–THEN Rules and Interval Type-2 Fuzzy Sets. IEEE Transactions On Fuzzy Systems 2011; 19(1): 136-151. DOI: 10.1109/TFUZZ.2010.2088128
133. [133] Wu, D, Mendel, JM, Joo, J. Linguistic Summarization Using IF-THEN Rules. In 2010 IEEE Xplore, IEEE International Conference On Fuzzy Systems; 18-23 July 2010: IEEE, pp. 1-8. DOI: 10.1109/FUZZY.2010.5584500
134. [134] National Aeronautics and Space Administration (NASA) 2016 NASA Systems Engineering Handbook, NASA SP-2016-6105 Rev2 supersedes SP-2007-6105 Rev 1, NASA Headquarters, Washington, DC.
135. [135] National Academies of Sciences, Engineering, and Medicine. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press., 2016. DOI: 10.17226/21873.
136. [136] Hyndman, RJ. A brief history of forecasting competitions. International Journal of Forecasting 2020; 36(1): 7-14. DOI: 10.1016/j.ijforecast.2019.03.015
137. [137] Fildes, R. The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion. International Journal of Forecasting 2006; 22: 415– 432. DOI: 10.1016/j.ijforecast.2006.03.002
138. [138] Ianchovichina, E, Ivanic, M. Economic Effects of the Syrian War and the Spread of the Islamic State on the Levant. World Bank Group Middle East and North Africa Region Office of the Chief Economist December 2014, Policy Research Working Paper 7135; WPS7135
139. [139] Wickham, H, Averick, M, Bryan, J, Chang, W, McGowan, LD, François, R, Grolemund, G, Hayes, A, Henry, L, Hester, J, Kuhn, M, Pedersen ,TL, Miller, E, Bache, SM, Müller, K, Ooms, J, Robinson, D, Seidel, DP, Spinu, V, Takahashi, K, Vaughan, D, Wilke, C, Woo, K, Yutani, H. Welcome to the tidyverse. Journal of Open Source Software 2019; 4(43): 1686. DOI: 10.21105/joss.01686.
140. [140] Kuhn M, Silge J. Package Tidymodels developed in 2020 by Kuhn M, Wickham H, and Rstudio In Tidy Modeling with R: A Framework for Modeling in the Tidyverse. California: O'Reilly Media, Inc. 2022. ISBN 9781492096481
141. [141] Lanzetta VB. Package ggExtra developed in 2016 by Attali D, and Baker C. In R Data Visualization Recipes: A cookbook with 65+ data visualization recipes for smarter decision-making. Birmingham: Packt Publishing. 2017. ISBN-13 978-1788398312
142. [142] Hyndman, RJ, Khandakar, Y. Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software 2008; 27(3): 1–22.
143. [143] Hyndman, R, Athanasopoulos, G, Bergmeir, C, Caceres, G, Chhay, L, O'Hara-Wild, M, Petropoulos, F, Razbash, S, Wang, E, Yasmeen, F. forecast: Forecasting functions for time series and linear models. R package version 2022; 8(16).
144. [144] Kuhn M, Silge J. Package readr developed in 2022 by Wickham H, Hester J, Francois R, Bryan J, Bearrows S, RStudio, Mandreyel, Jylanki J, and Jorgensen M In Tidy Modeling with R: A Framework for Modeling in the Tidyverse. California: O'Reilly Media, Inc. 2022. ISBN 9781492096481
145. [145] Kuhn M, Silge J. Package vroom developed in 2021 by Hester J, Wickham H, Bryan J, Mandreyel, Jylanki J, Jorgensen M, and Rstudio In Tidy Modeling with R: A Framework for Modeling in the Tidyverse. California: O'Reilly Media, Inc. 2022. ISBN 9781492096481
146. [146] Kuhn, M, Silge, JR: A language and environment for statistical computing developed in 2021 by R Core Team In Tidy Modeling with R: A Framework for Modeling in the Tidyverse. California: O'Reilly Media, Inc. 2022. ISBN 9781492096481
147. [147] Kuhn, M, Silge, J. Package parsnip developed in 2022 by Kuhn M, Vaughan D, Hvitfeldt E, and Rstudio In Tidy Modeling with R: A Framework for Modeling in the Tidyverse. California: O'Reilly Media, Inc. 2022. ISBN 9781492096481
148. [148] Friedman, J, Hastie, T, Tibshirani, R. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software 2010; 33(1): 1-22.
149. [149] Kuhn, M, Silge, J. Package yardstick developed in 2021 by Kuhn M, Vaughan D, and Rstudio In Tidy Modeling with R: A Framework for Modeling in the Tidyverse. California: O'Reilly Media, Inc. 2022. ISBN 9781492096481
150. [150] Wickham, H. ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag, 2016. ISBN: 978-3-319-24277-4
151. [151] Healy, K. Data Visualization A Practical Introduction. New Jersey: Princeton University Press, 2018. ISBN-13: 9780691181622
152. [152] Dalgaard, P. Introductory Statistics with R. New York: Springer, 2008. ISBN 978-0-387-79053-4 DOI: 10.1007/978-0-387-79054-1
153. [153] Hyndman, RJ, Athanasopoulos, G. Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia, 2021.
154. [154] Kuhn, M, Johnson, K. Applied Predictive Modeling. New York, USA: Springer, 2013.
155. [155] Kroese, DP, Botev, ZI, Taimre, T, Vaisman, R. Data Science and Machine Learning Mathematicaland Statistical Methods. New York: Chapman and Hall/CRC, 2020. ISBN 9781138492530
156. [156] Mohri, M, Rostamizadeh, A, Talwalkar, A. Foundations of Machine Learning. Cambridge, London: The MIT Press, 2018.
157. [157] Dunn, PK, Smyth, GK. Generalized Linear Models With Examples in R. New York, U.S.A.: Springer, 2018.
158. [158] Brownlee, J. Master Machine Learning Algorithms Discover How They Work and Implement Them From Scracth. Jason Brownlee, 2016.
159. [159] Lutkepohl, H. New Introduction to Multiple Time Series Analysis. Berlin, Germany: Springer, 2018.
160. [160] Box, GEP, Jenkins, GM, Reinsel, GC, Ljung, GM. Time Series Analysis Forecasting and Control, New Jersey, U.S.A.: John Wiley & Sons, 2016.
161. [161] Saracoglu, BO. Initialization of profile and social network analyses robot and platform with a concise systematic review. Machine Learning with Applications 2022; 7: 100249. DOI: 10.1016/j.mlwa.2022.100249