Generative Adversarial Network-Assisted Framework for Power Management

Suthar S, Cherukuri SHC, Pindoriya NM. Peer-to-peer energy trading in smart grid: frameworks, implementation methodologies, and demonstration projects. Electr Power Syst Res. 2023;214:108907.

Google Scholar 

Bao G, Xu RA, Data-Driven. Energy management strategy based on deep reinforcement learning for microgrid systems. Cogn Comput. 2023;15(2):739–50.

Google Scholar 

Tahir KA, Zamorano M, García JO. Scientific mapping of optimisation applied to microgrids integrated with renewable energy systems. Int J Electr Power Energy Syst. 2023;145:108698.

Google Scholar 

Talaat M, Elkholy MH, Alblawi A, et al. Artificial intelligence applications for microgrids integration and management of hybrid renewable energy sources. Artif Intell Rev. 2023;56:10557–611. https://doi.org/10.1007/s10462-023-10410-w.

Article  Google Scholar 

Peng T, Li Y, Song Z, Fu Y, Nazir MS, Zhang C. Hybrid intelligent deep learning model for solar radiation forecasting using optimal variational mode decomposition and evolutionary deep belief network-online sequential extreme learning machine. J Building Eng. 2023;76:107227.

Google Scholar 

Zheng J, Du J, Wang B, Klemeš JJ, Liao Q, Liang Y. A hybrid framework for forecasting power generation of multiple renewable energy sources. Renew Sustain Energy Rev. 2023;172:113046.

Google Scholar 

Psarros GN, Papathanassiou SA. Generation scheduling in island systems with variable renewable energy sources: A literature review. Renew Energy. 2023;205:1105–24.

Google Scholar 

Saxena N, Kumar R, Rao YK, Mondloe DS, Dhapekar NK, Sharma A, et al. Hybrid KNN-SVM machine learning approach for solar power forecasting. Environ Challenges. 2024;14:100838.

Google Scholar 

Guan S, Wang Y, Liu L, Gao J, Xu Z, Kan S. Ultra-short-term wind power prediction method based on FTI-VACA-XGB model. Expert Syst Appl. 2024;235:121185.

Google Scholar 

Wu J, Nguyen S, Alahakoon D, De Silva D, Mills N, Rathnayaka P, et al. A comparative analysis of machine learning-based Energy Baseline models across multiple building types. Energies. 2024;17(6):1285.

Google Scholar 

Shi J, Wang Z. A hybrid forecast model for household electric power by Fusing Landmark-based spectral clustering and deep learning. Sustainability. 2022;14(15):9255.

Google Scholar 

Akhtar S, Shahzad S, Zaheer A, Ullah HS, Kilic H, Gono R, et al. Short-term load forecasting models: a review of challenges, progress, and the road ahead. Energies. 2023;16(10):4060.

Google Scholar 

Mellit A, Massi Pavan A, Ogliari E, Leva S, Lughi V. Advanced methods for photovoltaic output power forecasting: a review. Appl Sci. 2020;10(2):487.

Google Scholar 

Mosavi A, Salimi M, Faizollahzadeh Ardabili S, Rabczuk T, Shamshirband S, Varkonyi-Koczy AR. State of the art of machine learning models in energy systems, a systematic review. Energies. 2019;12(7):1301.

Google Scholar 

Ferrero Bermejo J, Gomez Fernandez JF, Olivencia Polo F, Crespo Marquez A. A review of the use of artificial neural network models for energy and reliability prediction. A study of the solar PV, hydraulic and wind energy sources. Appl Sci. 2019;9(9):1844.

Google Scholar 

Naz A, Javaid N, Rasheed MB, Haseeb A, Alhussein M, Aurangzeb K. Game theoretical energy management with storage capacity optimization and photo-voltaic cell generated power forecasting in micro grid. Sustainability. 2019;11(10):2763.

Google Scholar 

Khare V, Nema S, Baredar P. Solar–wind hybrid renewable energy system: a review. Renew Sustain Energy Rev. 2016;58:23–33.

Google Scholar 

Ahmed A, Khalid M. A review on the selected applications of forecasting models in renewable power systems. Renew Sustain Energy Rev. 2019;100:9–21.

Google Scholar 

Abiyev R, Abizada S. Type-2 fuzzy wavelet neural network for estimation energy performance of residential buildings. Soft Comput. 2021;25(16):11175–90.

Google Scholar 

Dai Y, Zhao P. A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization. Appl Energy. 2020;279:115332.

Google Scholar 

Li C. Designing a short-term load forecasting model in the urban smart grid system. Appl Energy. 2020;266:114850.

Google Scholar 

Dietrich B, Walther J, Weigold M, Abele E. Machine learning based very short term load forecasting of machine tools. Appl Energy. 2020;276:115440.

Google Scholar 

Wang Z, Hong T, Piette MA. Building thermal load prediction through shallow machine learning and deep learning. Appl Energy. 2020;263:114683.

Google Scholar 

Liu C, Sun B, Zhang C, Li F. A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine. Appl Energy. 2020;275:115383.

Google Scholar 

Abdel-Basset M, Hawash H, Sallam K, Askar S, Abouhawwash M. STLF-Net: two-stream deep network for short-term load forecasting in residential buildings. J King Saud Univ - Comput Inf Sci. 2022;34(7):4296–311.

Google Scholar 

Khan SU, Khan N, Ullah FUM, Kim MJ, Lee MY, Baik SW. Towards intelligent building energy management: AI-based framework for power consumption and generation forecasting. Energy Build. 2023;279:112705.

Google Scholar 

Qin J. Experimental and analysis on household electronic power consumption. Energy Rep. 2022;8:705–9.

Google Scholar 

Khan N, Khan SU, Ullah FUM, Lee MY, Baik SW. AI-assisted hybrid approach for energy management in IoT-based smart microgrid. IEEE Internet of Things J. 2023;10(21):18861–75.

Google Scholar 

Ullah FUM, Ullah A, Khan N, Lee MY, Rho S, Baik SW. Deep learning-assisted short-term power load forecasting using deep convolutional LSTM and stacked GRU. Complexity. 2022;2022:2993184.

Google Scholar 

Ni Z, Zhang C, Karlsson M, Gong S. A study of deep learning-based multi-horizon building energy forecasting. Energy Build. 2024;303:113810.

Google Scholar 

Kim T, Lee D, Hwangbo S. A deep-learning framework for forecasting renewable demands using variational auto-encoder and bidirectional long short-term memory. Sustain Energy Grids Netw. 2024;38:101245.

Google Scholar 

Guo M, Lv R, Miao Z, Fei F, Fu Z, Wu E, et al. Load forecasting and operation optimization of ice-storage air conditioners based on improved deep-belief network. Processes. 2024;12(3):523.

Google Scholar 

Gonçalves R, Ribeiro VM, Pereira FL. Variable Split Convolutional attention: a novel deep learning model applied to the household electric power consumption. Energy. 2023;274:127321.

Google Scholar 

Wang H, Lei Z, Zhang X, Zhou B, Peng J. A review of deep learning for renewable energy forecasting. Energy Conv Manag. 2019;198:111799.

Google Scholar 

Zhang S, Chen Y, Xiao J, Zhang W, Feng R. Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism. Renew Energy. 2021;174:688–704.

Google Scholar 

Malik H, Yadav AK. A novel hybrid approach based on relief algorithm and fuzzy reinforcement learning approach for predicting wind speed. Sustain Energy Technol Assess. 2021;43:100920.

Google Scholar 

Kumari P, Toshniwal D. Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting. Appl Energy. 2021;295:117061.

Google Scholar 

Lamedica R, Santini E, Ruvio A, Palagi L, Rossetta I. A MILP methodology to optimize sizing of PV-Wind renewable energy systems. Energy. 2018;165:385–98.

Google Scholar 

Aurangzeb K, Aslam S, Haider SI, Mohsin SM, Islam Su, Khattak HA, et al. Energy forecasting using multiheaded convolutional neural networks in efficient renewable energy resources equipped with energy storage system. Trans Emerg Telecommun. 2022;33(2):e3837.

Google Scholar 

Li J, Deng D, Zhao J, Cai D, Hu W, Zhang M, Huang Q. A novel hybrid short-term load forecasting method of smart grid using MLR and LSTM neural network. IEEE Trans Industr Inform. 2020;17(4):2443–52.

Google Scholar 

Cascone L, Sadiq S, Ullah S, Mirjalili S, Siddiqui HUR, Umer M. Predicting Household Electric Power Consumption using multi-step Time Series with Convolutional LSTM. Big Data Res. 2023;31:100360.

Google Scholar 

Heydari A, Nezhad MM, Pirshayan E, Garcia DA, Keynia F, De Santoli L. Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm. Appl Energy. 2020;277:115503.

Google Scholar 

He F, Zhou J, Mo L, Feng K, Liu G, He Z. Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest. Appl Energy. 2020;262:114396.

Google Scholar 

Kong X, Li C, Wang C, Zhang Y, Zhang J. Short-term electrical load forecasting based on error correction using dynamic mode decomposition. Appl Energy. 2020;261:114368.

Google Scholar 

Hafeez G, Alimgeer KS, Khan I. Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid. Appl Energy. 2020;269:114915.

Google Scholar 

Wang Y, Chen J, Chen X, Zeng X, Kong Y, Sun S, et al. Short-term load forecasting for industrial customers based on TCN-LightGBM. IEEE Trans Power Syst. 2020;36(3):1984–97.

Google Scholar 

Somu N, MR GR, Ramamritham K. A hybrid model for building energy consumption forecasting using long short term memory networks. Appl Energy. 2020;261:114131.

Google Scholar 

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks. Commun ACM. 2020;63(11):139–44.

MathSciNet  Google Scholar 

Zhao R, Wang D, Yan R, Mao K, Shen F, Wang J. Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Trans Industr Electron. 2017;65(2):1539–48.

Google Scholar 

Zhang L, Zhang J, Gao W, Bai F, Li N, Ghadimi N. A deep learning outline aimed at prompt skin cancer detection utilizing gated recurrent unit networks and improved orca predation algorithm. Biomed Signal Process Control. 2024;90:105858.

Google Scholar 

Zamee MA, Won D. Novel mode adaptive artificial neural network for dynamic learning: application in renewable energy sources power generation prediction. Energies. 2020;13(23):6405.

Google Scholar 

Centre DS, DKASC. Alice Springs, eco-Kinetics, 26.5 kW, mono-Si, Dual, 2010: DKA Solar Centre; [cited 2022]. https://dkasolarcentre.com.au/source/alice-springs/dka-m11-3-phase.

Georges H, Berard A. Individual household electric power consumption. UCI Machine Learning Repository. 2012. https://doi.org/10.24432/C58K54.

Article  Google Scholar 

Wang Y, Zou R, Liu F, Zhang L, Liu Q. A review of wind speed and wind power forecasting with deep neural networks. Appl Energy. 2021;304:117766.

Google Scholar 

Khan N, Haq IU, Khan SU, Rho S, Lee MY, Baik SW. DB-Net: a novel dilated CNN based multi-step forecasting model for power consumption in integrated local energy systems. Int J Electr Power Energy Syst. 2021;133:107023.

Google Scholar 

Ullah FUM, Khan N, Hussain T, Lee MY, Baik SW. Diving deep into short-term electricity load forecasting: comparative analysis and a novel framework. Mathematics. 2021;9(6):611.

Google Scholar 

Haq IU, Ullah A, Khan SU, Khan N, Lee MY, Rho S, et al. Sequential learning-based energy consumption prediction model for residential and commercial sectors. Mathematics. 2021;9(6):605.

Google Scholar 

Kim T-Y, Cho S-B. Predicting residential energy consumption using CNN-LSTM neural networks. Energy. 2019;182:72–81.

Google Scholar 

Hebrail G, Berard A. Individual household electric power consumption data set. É d France, Ed, ed: UCI Machine Learning Repository. 2012.

Google Scholar 

Comments (0)

No login
gif