Energy Demand Forecasting Using Fused Machine Learning Approaches

Abstract

The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, transmission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to feature-based data provided a better result. Lastly, decision-based fusion applied to both datasets to predict power consumption through smart meters and get better results than previous techniques. The established model smart meter with automatic load control increases the effectiveness of energy management. The proposed EDF-FMLA model achieved 90.70 accuracy for predicting energy consumption with a smart meter which is better than the existing approaches.

Incorporating causality in energy consumption forecasting using deep neural networks

“…compared future energy predictions using the ARIMA and Random Forest models, and the results show that Random forest was better performing for the long-term prediction between the two models. Other examples of using basic machine learning algorithms include Murat and Ceylan (2006) using ANN to predict the energy demand in the transport sector; Kankal and Uzlu (2017) also using ANN for long-term energy demand forecast in Turkey; Ferlito et al (2015) using ANN to forecast a building's energy consumption; Lu et al (2021) using SVM for forecasting the energy consumption in the USA; Ghazal et al (2022) using IoT data and fusion of SVM algorithms to predict the industrial energy consumption and; Jana and Ghosh (2022) using discrete wavelet transform and ensemble machine learning algorithms to forecast natural gas prices and demand. Forouzandeh et al (2022) also used ensemble machine learning algorithms to predict room energy demand.…”

Studying the Metaverse Effect on its Users

“…Another article that discusses the negative effects of Metaverse on users comes from an article written by Jeon Joo-Eun [3]. The article's methodology includes taking a sample of Metaverse-platform users and integrated a multiple regression to test the impact of the respondents' commitment and relationship with each Metaverse platform [64,65,66,67].…”

“…In the 28 th of October 2021, the CEO of Facebook, Mark Zuckerberg, announced during a virtual event that the company is set to rebrand itself as "Meta," thus every application and software that run under Facebook such as Facebook itself, Instagram and WhatsApp will be integrated into the "Metaverse" operating system of Web 3.0. According to Salvador Rodriguez [1], the concept of "Metaverse" is referred as an extension of the real world turned virtual [1,2,3,4].…”

The Internet’s Role in Undermining the Credibility of the Healthcare Industry

“…Despite the innumerable benefits that this technological advancement has provided to societies around the world, there are significant drawbacks to the vast amount of readily available information at the hands of average people. This has been made even more apparent with the rampant spread of misinformation during the height of the COVID-19 pandemic [1,2,3,4,5]. The overarching issue is that the internet has produced an environment where people can take their healthcare into their own hands, as opposed to relying on licensed medical professionals for diagnosis, advice, and treatment.…”