Open Access

ARTICLE

Forecasting of Appliances House in a Low-Energy Depend on Grey Wolf Optimizer

Hatim G. Zaini^*

Computer Engineering Department, College of Computer and Information Technology, Taif University, Al Huwaya, Taif 26571, Saudi Arabia

* Corresponding Author: Hatim G. Zaini. Email:

(This article belongs to the Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)

Computers, Materials & Continua 2022, 71(2), 2303-2314. https://doi.org/10.32604/cmc.2022.021998

Received 23 July 2021; Accepted 30 August 2021; Issue published 07 December 2021

Abstract

This paper gives and analyses data-driven prediction models for the energy usage of appliances. Data utilized include readings of temperature and humidity sensors from a wireless network. The building envelope is meant to minimize energy demand or the energy required to power the house independent of the appliance and mechanical system efficiency. Approximating a mapping function between the input variables and the continuous output variable is the work of regression. The paper discusses the forecasting framework FOPF (Feature Optimization Prediction Framework), which includes feature selection optimization: by removing non-predictive parameters to choose the best-selected feature hybrid optimization technique has been approached. k-nearest neighbors (KNN) Ensemble Prediction Models for the data of the energy use of appliances have been tested against some bases machine learning algorithms. The comparison study showed the powerful, best accuracy and lowest error of KNN with RMSE = 0.0078. Finally, the suggested ensemble model's performance is assessed using a one-way analysis of variance (ANOVA) test and the Wilcoxon Signed Rank Test. (Two-tailed P-value: 0.0001).

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