Lightweight sustainable intelligent load forecasting platform for smart grid applications

• Published in: Sustainable computing informatics and systems 2020-03, Vol.25, p.100356, Article 100356
• Main Authors: Mukherjee, Amartya, Mukherjee, Prateeti, Dey, Nilanjan, De, Debashis, Panigrahi, B.K
• Format: Article
• Language: English
• Subjects: kNN; Logistic regression; Random forest; RBF; Smart grid; SVR
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2019.100356

•This work gives a direction towards the implementation of the sustainable, miniature, low cost and lightweight load forecasting embedded platform.•The work finds out the most suitable machine learning model for lightweight Raspberry Pi device.•The result produces by models with minimum RMSE score of 0.09012, minimum execution time of 27.85s and minimum system resource utilization.•Prototyping platform in the work performs comparative study of the performance of the machine learning algorithm.•The work gives a future direction towards the intelligent embedded system for load forecasting. 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.