Analysis of smart meter data for energy waste management

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Smart meters enable the high-frequency measurement and wireless communication of energy consumption, facilitating the digitalization of the energy industry, reducing operational costs and lowering carbon emission. Recently, artificial intelligence (AI) has emerged as an important tool for the analysis of smart meter data, supporting the transition to renewable energy sources, optimizing the energy supply through demand-response programs, and offering insights into energy usage patterns in homes through non-intrusive load monitoring (NILM). However, such precise data analysis has the power to reveal sensitive information about behavioral routines and personal activity, raising critical ethical challenges which may hurt public trust in the AI system. Motivated by these challenges, this chapter explores the development of trustworthy AI mechanisms for smart meter data analytics. Trustworthy AI enhances user privacy, adapts to changing usage patterns, and improves system transparency thereby facilitating a smoother transition to energy efficiency. We illustrate how privacy-preserving techniques can be used to protect user data while preserving the utility of AI models. The chapter further investigates how AI robustness can be enhanced to handle varied and dynamic energy usage patterns. Moreover, we emphasize the need for transparency and explainability in AI systems to ensure decision-making processes are understandable and justifiable, a requirement that is rarely fulfilled due to the complexity of AI algorithms. In summary, this chapter will discuss the types of AI approaches that leverage smart meter data, the ethical concerns they raise, and innovative solutions to overcome these difficulties.
Original languageEnglish
Title of host publicationArtificial Intelligence for Sustainability
Subtitle of host publicationInnovations in Business and Financial Services
EditorsThomas Walker, Stefan Wendt, Sherif Goubran, Tyler Schwartz
Place of PublicationCham, Switzerland
PublisherPalgrave Macmillan Ltd.
Chapter8
Pages153-173
Number of pages21
ISBN (Electronic)9783031499791
ISBN (Print)9783031499784
DOIs
Publication statusPublished - 24 Jan 2024

Keywords

  • non-intrusive load monitoring
  • energy disaggregation
  • deep learning
  • smart meters
  • trustworthy AI

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