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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Indonesian Journal of Electrical Engineering and Computer Science
Pradhan, Rudranarayan
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Computer Science & IT

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Load forecasting of electrical parameters: an effective approach towards optimization of electric load Mishra, Debani Prasad; Pradhan, Rudranarayan; Priyadarshini, Ananya; Das, Subha Ranjan; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp708-716

Abstract

The increasing need for energy and the increasing cost of electricity have prompted the development of smart energy optimization systems that can help consumers reduce their electricity consumption and minimize costs. These systems are developed on the concept of a “smart grid” which is a digitalized and intelligent energy network that provides help in the efficient distribution of energy. Load forecasting plays a crucial role in the precise prediction of uncontrollable electrical load. Long-term load analysis predicts a load of more than one year and helps in the planning of power systems whereas short-term and medium-term load forecasting helps in the supply and distribution of load, maintenance of load system, ensuring safety, continuous electricity generation, and cost management. Machine learning (ML) focuses on the development of smart energy optimization systems by enabling intuitive decision-making and reciprocation to sudden variations in consumer energy demands. This study focuses on the consumption of consumer electricity and provides a solution regarding the optimized methods that will predict future consumption based on previous data and help in reducing costs and preserving renewable energy. This research promotes sustainable energy usage. The use of ML models enables intelligent decision-making and accurate predictions, making the system an effective tool for managing electricity consumption.
Predictive modeling of electric vehicle loads through driving behavior analysis Mishra, Debani Prasad; Pradhan, Rudranarayan; Singh, Saksham; Singh, Anurag; Kumar, Ayush; Salkuti, Surender Reddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1431-1439

Abstract

Electric vehicles (EVs) can potentially be integrated into microgrids via vehicle-to-grid (V2G) technology, which enhances the energy system's stability and durability. This paper provides an in-depth examination and evaluation of V2G integration in microgrid systems. It analyses the present state of research as well as possible uses, challenges, and directions for V2G technology in the future. This article addresses the technological, economic, and regulatory aspects of implementing V2G and provides case studies and pilot projects to shed light on potential benefits and barriers associated with its adoption. The research highlights how V2G contributes to more efficient integration of renewable energy sources, grid stabilization, and cost savings for EV owners. It also addresses the latest developments in technology and proposed laws aimed at encouraging growing applications of V2G.
Co-Authors Das, Subha Ranjan Kumar, Ayush Mishra, Debani Prasad Priyadarshini, Ananya Salkuti, Surender Reddy Singh, Anurag Singh, Saksham