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The industrial revolution has elevated science and engineering to foster the development of Image Processing and Artificial Intelligence (AI) and put the visualization of information on an even higher pedestal. Yet, the demands of the industrial age have contributed to an ever-growing wildfire of climate change, sparking a revolution in energy efficiency research. With the aim to advance energy efficiency research from an AI standpoint, a novel transformation of raw-formatted data repositories, known as data lakes, into multi-dimensional visualizations data coupled with computationally lightweight, edge-based AI implementations are proposed as means to understand the energy consumption patterns in buildings. As a novel method of understanding energy data visually, current results comprise a Multi-Dimensional Gramian Angular Field (GAF) representation of energy data as both 2D and 3D interactive forms. Moreover, a case study on deep learning classification employed on ODROID-XU4 yields ~90% accuracy and a classification rate of 17.5 msec/image.

https://attend.ieee.org/dsc-2022/sicsa-event/ Deep Learning (DL) is increasingly empowering technology and engineering in a plethora of ways, especially when big data processing is a core requirement. Many challenges, however, arise when solely depending on cloud computing for Artificial Intelligence (AI), such as data privacy, communication latency, and power consumption. Despite the elevating popularity of edge computing, its overarching issue is not the lack of technical specifications in many edge computing platforms but the sparsity of comprehensive documentation on how to correctly utilize hardware to run ML and DL algorithms. Due to its specialized nature, installing the full version of TensorFlow, a common ML library, on an edge device is a complicated procedure that is seldom successful, due to the many dependent software libraries needed to be compatible with varying architectures of edge computing devices. Henceforth, in this paper, we present a novel technical guide on setting up the TensorFlow Lite, a lightweight version of TensorFlow and demonstrate a complete workflow of model training, validation, and testing on the ODROID-XU4. Results are presented for a case study on energy data classification using the outlined model show almost 7 times higher computational performance compared to cloud-based AI.

Data, in its purest nature, has an authority on the systems it accompanies by feeding an accurate representation of the observed reality. In energy efficiency, the underlying motivation for big data efforts revolves around the intrinsic need to understand end-user electric energy consumption and means to improve it. Hence, developing a rich, detailed, and realistic power consumption dataset entails a deliberate process of preparing the data collection environment, configuring proper Internet of Energy (IoE) sensors and managing the collected data. In this work, a novel power consumption dataset is presented in efforts to improve the state-of-the-art of energy efficiency research in buildings. The dataset is also accompanied by a two-dimensional (2D) counterpart produced using Gramian Angular Fields (GAF) that creates pictorial summaries from one-dimensional (1D) data. Data acquisition is carried out using the ODROIDXU4 edge computing hub, Home Assistant software, and a collection of smart plugs and sensors. A notable use case is presented to signify the merits of the data and its analysis tools to achieve computationally efficient classification.