Smart data processing for energy harvesting systems using artificial intelligence

Abstract

Recent substantial advancements in computational techniques, particularly in artificial intelligence (AI) and machine learning (ML), have raised the demand for smart self-powered devices. But since energy use is a worldwide issue that needs to be resolved immediately, cutting-edge technology should reduce energy consumption without affecting smart applications. Energy harvesting technology convert mechanical vibrations from the environment into electrical energy. Emerging AI technology which intends to meet the challenges of real world applications has open an interesting platform for some energy harvesting technologies, particularly piezoelectric nanogenerators (PENG) and triboelectric nanogenerators (TENG). In this context, advancements in AI technologies for data processing in PENG and TENG are discussed. A brief discussion about the combination of NG output with machine learning algorithms applied to a range of applications, such as robotics, intelligent security systems, medical systems, sports, acoustic sensors, and object recognition, is provided. The primary challenges and potential alternatives of these technologies are also discussed. © 2022 Elsevier Ltd