Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

TL;DR

This paper surveys data fusion strategies for energy efficiency in buildings, compares existing frameworks, and proposes a novel image-based appliance identification method with high accuracy, highlighting challenges and future directions.

Contribution

It provides a comprehensive taxonomy and comparison of data fusion methods in building energy systems and introduces a new texture-based appliance identification technique.

Findings

Achieved up to 99.68% accuracy in appliance identification

Provided a detailed taxonomy and comparison of data fusion strategies

Identified open challenges and future research directions

Abstract

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored.