Application of Metaheuristic Algorithms with Supervised Machine Learning for Accurate Power Consumption Prediction

van Ruijven BJ, De Cian E, Sue Wing I. Amplification of future energy demand growth due to climate change. Nat Commun. 2019;10(1):2762. https://doi.org/10.1038/s41467-019-10399-3.

Bhat JA. Renewable and non-renewable energy consumption—impact on economic growth and CO2 emissions in five emerging market economies. Environ Sci Pollut Res. 2018;25(35):35515–30.

Article  Google Scholar 

Yun S, Zhang Y, Xu Q, Liu J, Qin Y. Recent advance in new-generation integrated devices for energy harvesting and storage. Nano Energy. 2019;60:600–19.

Article  MATH  Google Scholar 

Zhang Z, Hong W-C, Li J. Electric load forecasting by hybrid self-recurrent support vector regression model with variational mode decomposition and improved cuckoo search algorithm. IEEE Access. 2020;8:14642–58.

Article  Google Scholar 

Zorpas AA. Strategy development in the framework of waste management. Sci Total Environ. 2020;716:137088.

Article  Google Scholar 

Olu-Ajayi R, Alaka H, Sulaimon I, Sunmola F, Ajayi S. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. J Build Eng. 2022;45:103406.

Article  Google Scholar 

Amasyali K, El-Gohary N. Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings. Renew Sustain Energy Rev. 2021;142:110714.

Article  Google Scholar 

Dong Z, Liu J, Liu B, Li K, Li X. Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification. Energy Build. 2021;241:110929.

Article  MATH  Google Scholar 

Amasyali K, El-Gohary NM. A review of data-driven building energy consumption prediction studies. Renew Sustain Energy Rev. 2018;81:1192–205.

Article  MATH  Google Scholar 

Li C, Ding Z, Zhao D, Yi J, Zhang G. Building energy consumption prediction: An extreme deep learning approach. Energies (Basel). 2017;10(10):1525.

Article  MATH  Google Scholar 

Zhao H, Magoulès F. A review on the prediction of building energy consumption. Renew Sustain Energy Rev. 2012;16(6):3586–92.

Article  MATH  Google Scholar 

Ali S, Bhargava A, Saxena A, Kumar P. A hybrid marine predator sine cosine algorithm for parameter selection of hybrid active power filter. Mathematics. 2023;11(3):598.

Article  MATH  Google Scholar 

Saxena A. A nonlinear hyperbolic optimized grey model for market clearing price prediction: Analysis and case study. Sustain Energy Grids Netw. 2024;38:101367.

Article  MATH  Google Scholar 

Madrid EA, Antonio N. Short-term electricity load forecasting with machine learning. Information. 2021;12(2):50.

Habbak H, Mahmoud M, Metwally K, Fouda MM, Ibrahem MI. Load forecasting techniques and their applications in smart grids. Energies (Basel). 2023;16(3):1480.

Article  Google Scholar 

Zhao Y, Zhang C, Zhang Y, Wang Z, Li J. A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis. Energy Built Environ. 2020;1(2):149–64.

Article  MATH  Google Scholar 

Wang P, Liu B, Hong T. Electric load forecasting with recency effect: a big data approach. Int J Forecast. 2016;32(3):585–97.

Article  MATH  Google Scholar 

Mounir N, Ouadi H, Jrhilifa I. Short-term electric load forecasting using an EMD-BI-LSTM approach for smart grid energy management system. Energy Build. 2023;288:113022. https://doi.org/10.1016/j.enbuild.2023.113022.

Article  Google Scholar 

Tarmanini C, Sarma N, Gezegin C, Ozgonenel O. Short term load forecasting based on ARIMA and ANN approaches. Energy Rep. 2023;9:550–7. https://doi.org/10.1016/j.egyr.2023.01.060.

Article  Google Scholar 

Yang Y, et al. The innovative optimization techniques for forecasting the energy consumption of buildings using the shuffled frog leaping algorithm and different neural networks. Energy. 2023;268:126548. https://doi.org/10.1016/j.energy.2022.126548.

Article  MATH  Google Scholar 

Malakouti SM. Babysitting hyperparameter optimization and 10-fold-cross-validation to enhance the performance of ML methods in Predicting Wind Speed and Energy Generation. Intell Syst Appl. 2023;19:200248.

Google Scholar 

Malakouti SM, Menhaj MB, Suratgar AA. The usage of 10-fold cross-validation and grid search to enhance ML methods performance in solar farm power generation prediction. Clean Eng Technol. 2023;15:100664.

Article  MATH  Google Scholar 

Malakouti SM. Improving the prediction of wind speed and power production of SCADA system with ensemble method and 10-fold cross-validation. Case Stud Chem Environ Eng. 2023;8:100351.

Article  MATH  Google Scholar 

Vapnik VN. The nature of statistical learning theory. New York, USA: Springer-Verlag; 1995. ISBN: 978-1-4757-3264-1. Edition No.: 2. https://doi.org/10.1007/978-1-4757-3264-1

Kiani J, Camp C, Pezeshk S. On the application of machine learning techniques to derive seismic fragility curves. Comput Struct. 2019;218:108–22. https://doi.org/10.1016/j.compstruc.2019.03.004.

Article  MATH  Google Scholar 

Freund Y. Boosting a weak learning algorithm by majority. Inf Comput. 1995;121(2):256–85.

Article  MathSciNet  MATH  Google Scholar 

Efraimidis PS, Spirakis PG. Weighted random sampling with a reservoir. Inf Process Lett. 2006;97(5):181–5.

Article  MathSciNet  MATH  Google Scholar 

Ferahtia S, et al. Red-tailed hawk algorithm for numerical optimization and real-world problems. Sci Rep. 2023;13(1):12950.

Article  Google Scholar 

Qais MH, Hasanien HM, Alghuwainem S. Transient search optimization: a new meta-heuristic optimization algorithm. Appl Intell. 2020;50:3926–41.

Article  MATH  Google Scholar 

Richter A, Boudinot BE, Garcia FH, Billen J, Economo EP, Beutel RG. Wonderfully weird: the head anatomy of the armadillo ant, Tatuidris tatusia (Hymenoptera: Formicidae: Agroecomyrmecinae), with evolutionary implications. Myrmecol News. 2023;33:35–53. https://doi.org/10.25849/myrmecol.news_033:035.

Alsayyed O, et al. Giant armadillo optimization: a new bio-inspired metaheuristic algorithm for solving optimization problems. Biomimetics. 2023;8(8):619.

Article  MATH  Google Scholar 

Wang S, Wang S, Chen H, Gu Q. Multi-energy load forecasting for regional integrated energy systems considering temporal dynamic and coupling characteristics. Energy. 2020;195:116964.

Article  MATH  Google Scholar 

Zhang P, Ma X, She K. A novel power-driven grey model with whale optimization algorithm and its application in forecasting the residential energy consumption in China. Complexity. 2019;2019(1):1510257.

Article  MATH  Google Scholar 

Shapi MKM, Ramli NA, Awalin LJ. Energy consumption prediction by using machine learning for smart building: Case study in Malaysia. Dev Built Environ. 2021;5:100037.

Article  Google Scholar 

Fumo N, Biswas MAR. Regression analysis for prediction of residential energy consumption. Renew Sustain Energy Rev. 2015;47:332–43.

Article  MATH  Google Scholar 

Salam A, El Hibaoui A. omparison of machine learning algorithms for the power consumption prediction:-case study of tetouan city–. In 2018 6th International Renewable and Sustainable Energy Conference (IRSEC). IEEE. 2018;2018:1–5.

Zeng A, Ho H, Yu Y. Prediction of building electricity usage using Gaussian Process Regression. J Build Eng. 2020;28:101054.

Pham A-D, Ngo N-T, Truong TTH, Huynh N-T, Truong N-S. Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability. J Clean Prod. 2020;260:121082.

Article  MATH  Google Scholar 

Comments (0)

No login
gif