• Mohit Kumar
• Rakesh Yadav
• Shivam Manwal
• Jyoti Kaushik

This paper offers the optimized value of sanctioned load/power allocation to smart homes that are having Smart Grid Infrastructure. Current methods allocate load in different slabs, but not all houses utilize the entire slab. In this paper, the load allocation values were predicted based on the previous year's consumption data. The dataset was taken from Kaggle and it contains electricity consumption data along with weather information of a smart home having 14 different appliances. Three metaheuristic algorithms viz. Grey Wolf Optimizer (GWO), Bat Algorithm (BA), and Hybrid Grey Wolf-Bat Algorithm (GWOBA) were used to optimize the parameters of the regression model created using Support Vector Regression (SVR). The results were studied for different temperature slabs. Accuracy for different algorithms was calculated by using cross-validation and it was found that GWOBA performed significantly better than the other two algorithms. It was observed that for GWOBA, the maximum accuracy gain observed was 12.1% whereas the maximum accuracy drop was 2% only. It was concluded that among all three algorithms, GWOBA is suitable for this problem.

Optimization, Metaheuristic algorithms, Support Vector Regression, Grey Wolf Optimizer, Bat Algorithm, Hybrid Grey Wolf-Bat Algorithm

"Optimization of Sanctioned Load in Smart Homes using Hybrid Grey Wolf-Bat Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.b397-b409, June-2021, Available :http://www.jetir.org/papers/JETIR2106192.pdf

"Optimization of Sanctioned Load in Smart Homes using Hybrid Grey Wolf-Bat Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppb397-b409, June-2021, Available at : http://www.jetir.org/papers/JETIR2106192.pdf