Optimization of Building Power Consumption Prediction Model
Keywords:Power Prediction, Machine learning, Hyper-Parameter, Meta-heuristics, Artificial Neural Networks, Ensemble methods, Genetic algorithm, Stochastic Gradient Decent
AbstractEnergy use and related greenhouse gas emissions have dramatically increased over the last century as a result of a range of factors, including both technological and population-based ones. Consequently, improving our energy efficiency is crucial if we are to attain global sustainability. Planning, management, optimization, and conservation are just a few of the many areas where predicting the energy use of a building is crucial. Due to their improved performance, robustness, and simplicity of implementation, data-driven models for energy forecasting have developed dramatically over the past few decades. Finding the right set of hyper-parameters is crucial to predict power usage using machine learning models. This raises the issue of complexity and extensive search spaces. We used the Metaheuristics approach to discover the ideal learning parameter values to address this issue. Through automatic parameter optimization, models optimized with metaheuristic methods typically perform better
Athanasios Tsanas, Angeliki Xifara, “Accurate quantitative estimation of the energy performance of residential
buildings using statical machine learning.” “Energy and Buildings.” 560-567
Dong-Lin Zheng, Li-Jun Yu, and Li-Zhen Wang, “Decision-making method for building energy efficiency retrofit
measures based on an improved analytic hierarchy process.” Journal of Renewable and Sustainable Energy.”
vol 11, Issue 4, pp. 1-18,2019/07/10.
D. Mariano-Hernandez, L.Hernandez-Callejo, A.Zorita-Lamadrid, O.Duque-Perez, F. Santos Garcia, “A review of
strategies for building energy management system: Model predictive control, demand side management,
optimization, and fault detect & Diagnosis.” vol. 33, pp. 1-12, 2020/03/12.
Hai-Xiang Zhao, Frederic Magoules, “A review on the prediction of building energy consumption.” “Renewable and
Sustainable Energy Review” vol 16, Issue 6, 2012, pages 3586-3592
J. Chou, D. Truong, “A novel metaheuristic optimizer inspired by the behavior of jellyfish in the ocean.” Applied
Mathematics and Computation” vol. 389, pp. 1-47, 2021/01/15.
Jason Runge and Radu Zmeureanu, “Forecasting Energy Use in Buildings Using Artificial Neural Networks: A
How to Cite
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.