An Artificial-Intelligence-Based Renewable Energy Prediction Program for Demand-Side Management in Smart Grids

Vinothini Arumugham (), Hayder M. A. Ghanimi, Denis A. Pustokhin, Irina V. Pustokhina, Vidya Sagar Ponnam, Meshal Alharbi, Parkavi Krishnamoorthy and Sudhakar Sengan
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Vinothini Arumugham: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India
Hayder M. A. Ghanimi: Biomedical Engineering Department, College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq
Denis A. Pustokhin: Department of Logistics, State University of Management, 109542 Moscow, Russia
Irina V. Pustokhina: Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, 117997 Moscow, Russia
Vidya Sagar Ponnam: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, Andhra Pradesh, India
Meshal Alharbi: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia
Parkavi Krishnamoorthy: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India
Sudhakar Sengan: Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli 627152, Tamil Nadu, India

Sustainability, 2023, vol. 15, issue 6, 1-26

Abstract: Technology advancements have enabled the capture of Renewable Energy Sources (RES) on a massive scale. Smart Grids (SGs) that combine conventional and RES are predicted as a sustainable method of power generation. Moreover, environmental conditions impact all RES, causing changes in the amount of electricity produced by these sources. Furthermore, availability is dependent on daily or annual cycles. Although smart meters allow real-time demand prediction, precise models that predict the electricity produced by RES are also required. Prediction Models (PMs) accurately guarantee grid stability, efficient scheduling, and energy management. For example, the SG must be smoothly transformed into the conventional energy source for that time and guarantee that the electricity generated meets the predicted demand if the model predicts a period of Renewable Energy (RE) loss. The literature also suggests scheduling methods for demand-supply matching and different learning-based PMs for sources of RE using open data sources. This paper developed a model that accurately replicates a microgrid, predicts demand and supply, seamlessly schedules power delivery to meet demand, and gives actionable insights into the SG system’s operation. Furthermore, this work develops the Demand Response Program (DRP) using improved incentive-based payment as cost suggestion packages. The test results are valued in different cases for optimizing operating costs through the multi-objective ant colony optimization algorithm (MOACO) with and without the input of the DRP.

Keywords: renewable energy; distributed energy resources; micro-grid system; deep learning; demand response programs; smart grid (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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