Deep learning-based novel ensemble method with best score transferred-adaptive neuro fuzzy inference system for energy consumption prediction.

ABSTRACT:

Background

Energy consumption predictions for smart homes and cities benefit many from homeowners to energy suppliers, allowing homeowners to understand and manage their future energy consumption, improve energy efficiency, and reduce energy costs. Predictions can help energy suppliers effectively distribute energy on demand. Therefore, from the past to the present, numerous methods have been conducted using collected data, employing both statistical and artificial intelligence (AI)-based approaches, to achieve successful energy consumption predictions.

Methods

This study proposes a deep learning-based novel ensemble (DLBNE) method with the best score transferred-adaptive neuro fuzzy inference system (BST-ANFIS) as a high-performance and robust approach for energy consumptio

SUBMITTER: Dagkurs B 

PROVIDER: S-EPMC11888908 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature