Abstract
In recent decades, the world has witnessed a great expansion in the world of technology and electronics, in addition to the tremendous development in various industries, which has led to an increase in the need for electrical energy significantly. Renewable energy generated from environmentally friendly sources such as energy (solar, water, windmills, etc.) is the solution. The alternative is to provide that energy, especially as it is clean energy that does not cause the emission of carbon dioxide, which pollutes the air and the environment in general. This work presents a software model for producing the largest amount of energy by developing one of the best prediction techniques and using multi-parameter objective functions, where the proposed model called zero to maximum energy based on developing gradient boosting machine (DMP-DGBM) consists of several stages. The problem of this work is divided into parts: The first part is related to programming challenges, while the second part is related to application challenges; as we know, the prediction techniques are split based on the scientific field into two fields: prediction techniques related to data mining and predictions related to deep learning techniques; this work deals with the first type of prediction technique. (a) One of the data mining prediction techniques is the gradient boosting machine characterized by many features that make it the best. These features are GBM gives high accuracy results and works with huge data stream of data, but on the other hand, the core of that algorithm is a decision tree (DT) that has many limitations such as requiring choosing the root of the tree, determining the maximum number of levels of the tree, and also having high computation and long time. Therefore, the first challenge of this paper is how can avoid these limitations (i.e., high computation and implementation time) of this algorithm and benefit from their features. (b) The problem of generating electrical energy from environmentally friendly sources with high efficiency is one of the most important challenges in this field. Therefore, the second challenge of this paper is how can avoid these limitations by building an efficient technique to predict maximum energy from solar energy. DMP-DGBM model consists of many stages applied through a stepwise style. The first stage presents capturing datasets from scientific site which contains the data related to both weather and solar plant. The second stage is preprocessing which contains multi-steps including: (a) merging between two datasets; (b) splitting readings into intervals and deleting the duplicate; and (c) applying Pearson’s correlation to the new dataset. In the third stage, the ZME-DGBM model is constructed based on developing gradient boosting techniques by replacing its kernel (i.e., decision tree function) with multi-parameter optimization functions. The stage begins with dividing the dataset into two sets using five cross-validation methods, and the training dataset is used to construct the DMP-DGBM models, while the testing dataset is used to evaluate them. Finally, the results of the DMP-DGBM are evaluated based on three measures (i.e., coefficient of determination, mean error, and root mean square error. The stage of constructing the predictor relied on replacing the GBM kernel with four different multi-parameter functions, as these were the parameters with the highest correlation with the target, and a threshold value of 0.95 was adopted as determining the importance of the parameters. The proposed model was characterized by giving the best results using a three-parameter function MPF4 for the GBM kernel, and those parameters were AC, TEM, and IRR, where the scale was (R2 = 0.9742), while for MSE = 0.0099 and RMSE = 0.0522 also the system is taken only 80 Ms to implement.
Data availability
Not applicable.
References
Al-Janabi S, Alkaim AF, Adel Z (2020) An Innovative synthesis of deep learning techniques (DCapsNet and DCOM) for generation electrical renewable energy from wind energy. Soft Comput 24:10943–10962. https://doi.org/10.1007/s00500-020-04905-9
Diezmartínez CV (2021) Clean energy transition in Mexico: policy recommendations for the deployment of energy storage technologies. Renew Sustain Energy Rev 135:87. https://doi.org/10.1016/j.rser.2020.110407
Al-Janabi S, Alkaim A, Al-Janabi E, Aljeboree A, Mustafa M (2021) Intelligent forecaster of concentrations (PM2.5, PM10, NO2, CO, O3, SO2) caused air pollution (IFCsAP). Neural Comput Appl. https://doi.org/10.1007/s00521-021-06067-7
Mahdi MA, Al-Janabi S (2020) A novel software to improve healthcare base on predictive analytics and mobile services for cloud data centers in lecture notes in networks and systems. Springer
Migdadi YKAA (2022) Identifying the best practices in hotel green supply chain management strategy: a global study. J Qual Assur Hosp Tour. https://doi.org/10.1080/1528008X.2022.2065657
Al-Janabi S, Salman AH (2021) Sensitive integration of multilevel optimization model in human activity recognition for smartphone and smartwatch applications. Big Data Min Anal 4(2):124–138. https://doi.org/10.26599/BDMA.2020.9020022.
Ahmad T, Chen H, Shah WA (2019) Effective bulk energy consumption control and management for power utilities using artificial intelligence techniques under conventional and renewable energy resources. Int J Electr Power Energy Syst 109:242–258. https://doi.org/10.1016/j.ijepes.2019.02.023
Touzani S, Granderson J, Fernandes S (2018) Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy and Build 158:1533–1543. https://doi.org/10.1016/j.enbuild.2017.11.039
Al-Janabi S, Alkaim A, Al-Janabi E et al (2021) Intelligent forecaster of concentrations (PM2.5, PM10, NO2, CO, O3, SO2) caused air pollution (IFCsAP). Neural Comput and Appl 33:14199–14229. https://doi.org/10.1007/s00521-021-06067-7
Hossny K, Magdi S, Soliman AY, Hossny AH (2020) Detecting explosives by PGNAA using KNN Regressors and decision tree classifier: a proof of concept. Progr Nucl Energy 124:87. https://doi.org/10.1016/j.pnucene.2020.103332
Rustam F, Khalid M, Aslam W, Rupapara V, Mehmood A, Choi GS (2021) A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis. PLoS ONE. https://doi.org/10.1371/journal.pone.0245909
Cotfas LA, Delcea C, Roxin I, Ioanǎş C, Gherai DS, Tajariol F (2021) The longest month: analyzing COVID-19 vaccination opinions dynamics from tweets in the month following the first vaccine announcement. IEEE Access 9:33203–33223. https://doi.org/10.1109/ACCESS.2021.3059821
Hao J (2020) Deep reinforcement learning for the optimization of building energy control and management.
Dogan A, Birant D (2021) Machine learning and data mining in manufacturing. in expert systems with applications (vol 166). Elsevier Ltd.
Das HS, Roy P (2019) A deep dive into deep learning techniques for solving spoken language identification problems In intelligent speech signal processing. Elsevier
Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53(8):5455–5516. https://doi.org/10.1007/s10462-020-09825-6
Ihab A-J and Samaher A-J (2002), Smart micro-grid model to generated renewable energy based on embedded intelligent and FPGA, Recent Advances in Material, Manufacturing, and Machine Learning: Proceedings of 1st International Conference (RAMMML-22), Volume 1 (1st ed.). CRC Press. https://doi.org/10.1201/9781003358596
Ihab A-J, Samaher A-J, Monaria H, and Saeed K (2022), Building integrated system to generation DC-power based on renewable energy,. Recent Advances in Material, Manufacturing, and Machine Learning: Proceedings of 1st International Conference (RAMMML-22), Volume 1 (1st ed.). CRC Press. https://doi.org/10.1201/9781003358596
Zhang G, Hu W, Cao D, Liu W, Huang R, Huang Q, Chen Z, Blaabjerg F (2021) Data-driven optimal energy management for a wind-solar-diesel-battery-reverse osmosis hybrid energy system using a deep reinforcement learning approach. Energy Convers Manage 227:113608. https://doi.org/10.1016/j.enconman.2020.113608
Razmjoo A, Gakenia Kaigutha L, Vaziri Rad MA, Marzband M, Davarpanah A, Denai M (2021) A Technical analysis investigating energy sustainability utilizing reliable renewable energy sources to reduce CO2 emissions in a high potential area. Renew Energy 164:46–57. https://doi.org/10.1016/j.renene.2020.09.042
Oryani B, Koo Y, Rezania S, Shafiee A (2021) Barriers to renewable energy technologies penetration: Perspective in Iran. Renew Energy 174:971–983. https://doi.org/10.1016/j.renene.2021.04.052
Li N, Su Z, Jerbi H, Abbassi R, Latifi M, Furukawa N (2021) Energy management and optimized operation of renewable sources and electric vehicles based on microgrid using hybrid gravitational search and pattern search algorithm. Sustain Cities Soc 75:103279. https://doi.org/10.1016/j.scs.2021.103279
Obara S, Ito Y, Okada M (2018) Optimization algorithm for power-source arrangement that levels the fluctuations in wide-area networks of renewable energy. Energy 142:447–461. https://doi.org/10.1016/j.energy.2017.10.038
Şahin U (2020) Projections of Turkey’s electricity generation and installed capacity from total renewable and hydro energy using fractional nonlinear grey Bernoulli model and its reduced forms. Sustain Prod Consumption 23:52–62. https://doi.org/10.1016/j.spc.2020.04.004
Haidar AMA, Fakhar A, Helwig A (2020) Sustainable energy planning for cost minimization of autonomous hybrid microgrid using combined multi-objective optimization algorithm. Sustain Cities Soc 62:102391. https://doi.org/10.1016/j.scs.2020.102391
Guerraiche K, Dekhici L, Chatelet E, Zeblah A (2021) Multi-objective electrical power system design optimization using a modified bat algorithm. Energies 14(13):3956
Zebari AY, Almufti SM, Abdulrahman CM (2020) Bat algorithm (BA): review, applications and modifications. Int J Scientif World 8(1):1
Ashari IF, Banjarnahor R, Farida DR, Aisyah SP, Dewi AP, Humaya N (2022) Application of data mining with the k-means clustering method and davies Bouldin index for grouping IMDB movies. J Appl Inf Comput 6(1):07–15
Singh P, Meena NK, Slowik A, Bishnoi SK (2020) Modified african buffalo optimization for strategic integration of battery energy storage in distribution networks. IEEE Access 8:14289–14301
Agrawal P, Alnowibet K, Mohamed AW (2022) Gaining-sharing knowledge based algorithm for solving stochastic programming problems. Computers, Mater Continua 71(2):2847
Xiong G, Yuan X, Mohamed AW, Chen J, Zhang J (2022) Improved binary gaining–sharing knowledge-based algorithm with mutation for fault section location in distribution networks. J Comput Des Eng 9(2):393–405
Suman GK, Guerrero JM, Roy OP (2021) Optimisation of solar/wind/bio-generator/diesel/battery based microgrids for rural areas: A PSO-GWO approach. Sustain Cities Soc 67:102723. https://doi.org/10.1016/j.scs.2021.102723
Al-Janabi S, Alkaim A (2022) A novel optimization algorithm (Lion-AYAD) to find optimal DNA protein synthesis. Egyptian Inf J. https://doi.org/10.1016/j.eij.2022.01.004
Zhao P, Gou F, Xu W, Wang J, Dai Y (2022) Multi-objective optimization of a renewable power supply system with underwater compressed air energy storage for seawater reverse osmosis under two different operation schemes. Renew Energy 181:71–90. https://doi.org/10.1016/j.renene.2021.09.041
Kharrich M, Mohammed OH, Alshammari N, Akherraz M (2021) Multi-objective optimization and the effect of the economic factors on the design of the microgrid hybrid system. Sustain Cities Soc 65:102646. https://doi.org/10.1016/j.scs.2020.102646
Haidar AM, Fakhar A, Helwig A (2020) Sustainable energy planning for cost minimization of autonomous hybrid microgrid using combined multi-objective optimization algorithm. Sustain Cities Soc 62:102391. https://doi.org/10.1016/j.scs.2020.102391
Kaabeche A, Bakelli Y (2019) Renewable hybrid system size optimization considering various electrochemical energy storage technologies. Energy Convers Manage 193:162–175. https://doi.org/10.1016/j.enconman.2019.04.064
Peng SL, Pal S, Huang L (eds) (2020). Springer International Publishing
Elkasaby A, Salah A, and Elfeky E (2017) Multiobjective optimization using genetic programming: reducing selection pressure by approximate dominance. In ICORES (pp. 424–429)
Stergiou K, Karakasidis TE (2021) Application of deep learning and chaos theory for load forecasting in Greece. Neural Comput Appl 33:16713–16731. https://doi.org/10.1007/s00521-021-06266-2
Patel VK, Savsani VJ, Tawhid MA (2019) Thermal system optimization. Springer
Trojovsky P., and Dehghani, M. (2022). Hybrid leader based optimization: a new stochastic optimization algorithm for solving optimization applications
Póvoa RC, Koshiyama AS, Dias DM, Souza PL, Horta BA (2020) Unimodal optimization using a genetic-programming-based method with periodic boundary conditions. Genet Progr Evolvable Mach 21(3):503–523
Dutta T, Bhattacharyya S, Dey S, Platos J (2020) Border collie optimization. IEEE Access 8:109177–109197
Al-Janabi S, Alkaim AF, Adel Z (2020) An Innovative synthesis of deep learning techniques (DCapsNet and DCOM) for generation electrical renewable energy from wind energy. Soft Comput 24(14):10943–10962. https://doi.org/10.1007/s00500-020-04905-9
Zebari AY, Almufti SM, Abdulrahman CM (2020) Bat algorithm (BA): review, applications and modifications. Int J Scientif World 8(1):1
Kicska G, Kiss A (2021) Comparing swarm intelligence algorithms for dimension reduction in machine learning. Big Data Cognit Comput 5(3):36
Freitas D, Lopes LG, Morgado-Dias F (2020) Particle swarm optimisation: a historical review up to the current developments. Entropy 22(3):362
Rana N, Latiff MSA, Abdulhamid SIM, Chiroma H (2020) Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments. Neural Comput Appl 32(20):16245–16277
Kumar V, Kumar D, Kaur M, Singh D, Idris SA, Alshazly H (2021) A novel binary seagull optimizer and its application to feature selection problem. IEEE Access 9:103481–103496
Hochreiter S, and Urgen Schmidhuber J ¨. (n.d.). Long short-term memory.
Abdel-Basset M, Abdle-Fatah L, Sangaiah AK (2019) An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Clust Comput 22(4):8319–8334
Mohamed AW, Hadi AA, Mohamed AK (2020) Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. Int J Mach Learn Cybern 11(7):1501–1529
Tan P-N, Steinbach M, Karpatne A, and Kumar V. (n.d.). Introduction to data mining instructor’s solution manual.
Ardianto R, Rivanie T, Alkhalifi Y, Septia Nugraha F, Gata W (2020) Sentiment analysis on e-sports for education curriculum using naive bayes and support vector machine In Jurnal Ilmu Komputer dan Informasi. J Computer Sci Inf 13:2
Salgotra R, Singh U, Saha S (2019) On some improved versions of whale optimization algorithm. Arab J Sci Eng 44(11):9653–9691
Reena S, Hariprasad K, Manojkumar R (2022) Multi-objective dynamic optimization of hybrid renewable energy systems. Chem Eng Process- Process Intensif 170:87. https://doi.org/10.1016/j.cep.2021.108663
Ning GY, Cao DQ (2021) Improved whale optimization algorithm for solving constrained optimization problems. Discr Dyn Nature Soc 54:7
Duan Y, Liu C, Li S, Guo X, Yang C (2022) Gradient-based elephant herding optimization for cluster analysis. Appl Intell 45:1–32
Ismaeel AA, Elshaarawy IA, Houssein EH, Ismail FH, Hassanien AE (2019) Enhanced elephant herding optimization for global optimization. IEEE Access 7:34738–34752
Al-Janabi S, Alhashmi S, Adel Z (2020) Design (More-G) model based on renewable energy and knowledge constraint. In: Farhaoui Y (ed) Big data and networks technologies BDNT 2019 lecture notes in networks and systems, vol 81. Springer
Li W, Wang GG, Alavi AH (2020) Learning-based elephant herding optimization algorithm for solving numerical optimization problems. Knowl-Based Syst 195:105675
Aslam S, Herodotou H, Mohsin SM, Javaid N, Ashraf N, Aslam S (2021) A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. In renewable and sustainable energy reviews (Vol 144). Elsevier Ltd.
Al-Janabi S, Alkaim AF (2020) A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation Springer. Soft Comput 24(1):555–569. https://doi.org/10.1007/s00500-019-03972-x
Igiri CP, Singh Y, Bhargava D, Shikaa S (2020) Improved African buffalo optimisation algorithm for petroleum product supply chain management. Int J Grid Util Comput 11(6):769–779
Author information
Authors and Affiliations
Contributions
All authors contributed to the study’s conception and design. Design the system was achieved by SA-J. Test and analysis were performed by SA-J and ZKA-J. The first draft of the manuscript was written by SA-J. All authors read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Al-Janabi, S., Al-Janabi, Z. Development of deep learning method for predicting DC power based on renewable solar energy and multi-parameters function. Neural Comput & Applic 35, 15273–15294 (2023). https://doi.org/10.1007/s00521-023-08480-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-023-08480-6