Data-Driven Technology Applications in Planning, Demand-Side Management, and Cybersecurity for Smart Household Community

Abstract

The need for data-driven technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL) in various sectors has been soaring for over a decade. The amount of data released by the smart grid itself has been enormous, making these cutting-edge technologies highly efficient and reliable. This article proposes an orderly review of data-driven technology applications for smart residential households. It underpins the importance of forecasting studies with demand-side management (DSM)-aided tools such as demand response (DR), over a secure energy transaction platform. For the publications reviewed, the outcomes suggest the urgent need for household-level forecasting as it accounts for only 21% of the publications reviewed while DL dominates the forecasting studies (57%) with scope towards its hybridization with decomposition techniques. Similarly, the DSM/DR domain needs to be actively implemented at the retail level over a secure network. The outcomes suggest that baseline prediction (4.76%) and self-learning DR (19%) are crucial but the least focused issues, hence AI/ML/DL could be the solutions. Likewise, scalability (24.3%) turns out to be the major issue for assessing the security of the utility grid. However, deep reinforcement learning (DRL) could be a suitable tool as it is adaptive, independent of the system dynamics, and works best in a model-free dynamic environment. The overall findings suggest that the smart household community is the least focused entity and needs prompt attention to address the associated challenges. Additionally, several distinct insights such as dataset features, model parameters, performance metrics, customer-centricity, customer diversity, and mitigation are mapped with applications. Besides, this article points out various shortcomings and tries to postulate probable solutions to the best of capacity.