Lightweight sustainable intelligent load forecasting platform for smart grid applications

Abstract With the global electricity demand witnessing a 3.1 percent jump in 2017, there is an increasing need for incorporating intermittent renewable energy sources and other alternative supply/demand management strategies into the supply grid networks. Short-term load forecasting models enable prediction of future power consumption, thereby encouraging shifting of loads and optimizing the use of stochastic power sources and stored energy. To make the electric grid system smart and sustainable, two-way communication between the utility and consumers must be set up and the working equipment must respond digitally to the quickly changing electric demand. The proposed work exploits the power of embedded systems to design a low-cost solution for interconnecting electrical and electronic devices, controlled by the intelligent Internet of Things (IoT) paradigms. This work primarily focuses on implementing standard regression and machine learning-based architectures for smart grid load analysis and forecasting. A state of the art ecosystem for a portable load forecasting device is proposed by means of low-cost, open-source hardware that is experimentally found to be functioning with a high degree of accuracy. Further, the performance of the classical and advanced machine learning models, emulated on the device, are analyzed on the basis of various parameters, including error percentage, execution time, CPU core temperatures, and resource utilization. Overall impressive performance is demonstrated by some specific machine learning models which are considered to be suitable for the proposed framework.