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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Applied Power Engineering (IJAPE) Indonesian Journal of Electrical Engineering and Computer Science
Debani Prasad Mishra
IIIT Bhubaneswar
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Global solar radiation forecast using an ensemble learning approach Debani Prasad Mishra; Subhrajit Jena; Rudranarayan Senapati; Atman Panigrahi; Surender Reddy Salkuti
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i1.pp496-505

Abstract

With the increase in demand for solar power, a solar power forecasting model is of maximum importance to allow a higher level of integration of non-conventional energy into the existing electricity grid. With the advancement in data availability, there’s a good time to use data-driven algorithms for enhanced prediction of solar energy generation. Gathering and analyzing data can predict solar energy generation and mitigate the impact of solar intermittency. During this research, we explore automatically creating prediction models that are site-specific utilizing machine learning to generate solar radiation from meteorological station weather forecast reports, and from the predicted solar radiation corresponding solar power output can be calculated depending upon the characteristics of the solar PV system used. The challenge is to enhance the accuracy of the forecast. Ensemble techniques like random forest (RF) and extreme gradient boosting (XGBoost) are well suited for solar radiation prediction as they improve stability as well as combine several machine learning models to reduce variation and bias which outperforms the majority of models, as a result making them a perfect model in the field of solar energy prediction.
Text grouping: a comprehensive guide Padarabinda Palai; Kaushiki Agrawal; Debani Prasad Mishra; Surender Reddy Salkuti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1476-1483

Abstract

Text keywords have huge variance and to bridge the gap between the country business segment which provides negligible information and the keywords that have a huge longtail it is imperative for us to categorize the queries that provide a middle ground and also serve a few other purposes. The paper will present those in-depth. Query categorization falls into the segment of 'Multi-Class Classification' in the domain of natural language processing (NLP). However, business requirements require the implementation of any technique that could provide as accurate results as possible. So, to solve this problem the paper discusses an amalgamation of approaches like TF-IDF (term frequency-inverse document frequency), neural networks, cosine similarity, transformers-all of which fix specific issues.
Distribution networks power loss allocation with various power factors Debani Prasad Mishra; Rudranarayan Senapati; Arun Kumar Sahoo; Jayanta Kumar Sahu; Surender Reddy Salkuti
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1234-1241

Abstract

The users of power distribution and transmission networks are generally guided to sustain advanced power factor (PF) of load as it is affecting the power loss of a feeder network where it separately owns major influence on electric charges layout. Therefore, some cautious loss allotment schemes are to be incorporated and an acceptable satisfying/penalizing policy for advanced/less PF users, independently. Keeping this in view, the mentioned article proposed a new scheme, i.e., the active power loss allocation (APLA) procedure which allows power loss to the system distributors by considering the load demands, topographical localities, and PFs. A newly modified procedure assigns inducements hardly to all the involved utilizers against change in load PF continuously, where it is evaluated via proper mathematical and statistical study. The efficiency of the newly modified APLA scheme is explored in two dissimilar frameworks of low PF using 33 bus system radially distributed network (RDN). The interpretation is in favor of examined transmitted, distributed, and allows generated PF to be verified subsequently. Comparatively, the results achieved highlight the originality of the present method compared with different standard schemes/frameworks.
Image classification using machine learning Debani Prasad Mishra; Sanhita Mishra; Smrutisikha Jena; Surender Reddy Salkuti
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1551-1558

Abstract

The objective of this paper is to implement different tools available in machine learning/artificial intelligence to classify faces and identify different features, highlights, and correlations or similarities between different celebrity faces which can apply in everyday security purposes to identity virtually if the authorized personnel is using certain access or not. The material present in this paper is a literature review of a machine learning model developed by the students. This is a classical problem of machine learning executed using a support vector machine. Images are separated based on sub-images. Each sub-image has been classified into a responsive class by an artificial neural network. The website then fetches the data from the back end and classifies the image into the corresponding personal.
Performance improvement of DC microgrids via adaptive neuro-fuzzy inference system -optimized AI-tuned fractional order proportional-integral-derivative controllers Debani Prasad Mishra; Sarita Samal; Manas Ranjan Sahu; Sonna Murari; Piyuskant Das; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp797-804

Abstract

This paper presents a novel approach to enhance the dynamic performance of direct current (DC) microgrids using an artificial intelligence (AI)-tuned fractional order proportional-integral-derivative (FO-PID) controller, further optimized through an adaptive neuro-fuzzy inference system (ANFIS). Conventional PID controllers tend to fail when it comes to dealing with microgrid environment-related non-linearities and uncertainties, particularly under changing load and generation situations. To remedy this, the suggested approach combines AI-tuned tuning algorithms for selecting initial parameters, and then ANFIS optimization to fine-tune the FOPID gains adaptively for better control precision. The performance of the hybrid control approach is tested through MATLAB simulations on a generic DC microgrid model that includes distributed energy resources, power electronic converters, and dynamic loads. Comparative evaluation against standard PID and independent FOPID controllers verifies remarkable advantages in terms of voltage regulation, stability, and transient response in various operating conditions. Amongst the achieved outcomes, it highlights the strength of the proposed ANFIS-optimized AI-tuned FOPID controller as a smart and robust strategy for real-time control of DC microgrids.
Predicting battery life performance using artificial intelligence techniques in electric vehicles Debani Prasad Mishra; Munavath Pavan Kalyan; Shivam Tyagi; Piyushjeet Piyushjeet; Shiv Grover; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp805-812

Abstract

Electric vehicles’ (EVs’ performance and sustainability are significantly influenced by the efficiency and lifespan of their lithium-ion batteries. This paper explores the critical factors affecting battery degradation, focusing on parameters such as charge cycles, thermal management, and voltage dynamics. Utilizing a dataset of 14 batteries, the study employs data-driven machine learning (ML) to predict the remaining useful life (RUL) of batteries. The ensemble-based regression model demonstrated superior predictive accuracy through comprehensive analysis, achieving R² values of 97.89% for training and 94.69% for testing. Feature importance analysis identified cycle index (CI) as the most critical determinant of battery health, followed by discharge time and voltage stability. Visualizations, including correlation heatmaps and residual plots, validate the robustness of the selected model. Additionally, sustainable charging strategies, such as steady current-steady voltage (also known as CC-CV), are highlighted for their role in enhancing battery longevity. This research offers actionable insights into battery management systems, providing a robust foundation for predictive maintenance and the development of sustainable electric mobility solutions.
Integrating IoT for advancing agriculture: innovations and implications for future surveys Debani Prasad Mishra; Rakesh Kumar Lenka; Aditya Kumar; Aditya Jasrotia; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp891-899

Abstract

The internet of things (IoT) is revolutionizing agriculture, offering a paradigm shift in how we cultivate crops and manage livestock. By integrating IoT devices such as sensors, drones, and smart machinery into farming practices, agricultural operations gain unprecedented levels of data driven insights and control. This abstract emphasizes the pivotal role of IoT in agriculture and its far-reaching implications for the future. IoT empowers farmers with real-time information on essential factors like moisture of soil, nutrient levels, weather patterns, and health of crops, helping make accurate decisions while optimizing resources. Through IoT-enabled monitoring and automation, farmers can remotely manage irrigation, pest control, and livestock health, reducing manual labor and minimizing environmental impact. The implications of IoT in agriculture extend beyond individual farms, shaping the future of food production on a global scale. With a burgeoning world population and climate change threatening traditional farming methods, IoT offers solutions for enhancing productivity, sustainability, and resilience in the face of emerging challenges. From precision agriculture to smart supply chains, the revolutionary prospect of IoT in agriculture promises to ensure food security, economic viability, and environmental stewardship for generations to come.
GSM based load monitoring system with ADL classification and smart meter design Debani Prasad Mishra; Rudranarayan Senapati; Rohit Kumar Swain; Subhankar Dash; Raj Alpha Swain; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp74-83

Abstract

This paper introduces a method for the classification of activities of daily living (ADL) by utilizing smart meter and smart switch data in a synergistic approach. Through the integration of these internet of things (IoT) devices, the paper aims to enhance the application of ADL classification. Guided by recent advancements in load monitoring and energy management systems, the methodology incorporates machine learning techniques to analyze data streams from both the smart meter and smart switch. Drawing inspiration from prepaid smart meter monitoring systems, IoT-based smart energy meters for optimizing energy usage, and energy metering chips with adaptable computing engines, our design incorporates diverse perspectives. Additionally, we consider the utilization of mobile communication for prepaid meters, remote detection of malfunctioning smart meters, and an empirical investigation into the acceptance of IoT-based smart meters. We substantiate our proposed approach through experimental results, showcasing its effectiveness in accurately classifying diverse ADL scenarios. This research contributes to the field of smart home technology by offering an advanced method for ADL classification. The integration of smart meter and smart switch data provides a comprehensive understanding of energy consumption patterns, opening avenues for improved energy management and informed decision-making within smart homes.
Renewable energy optimization for sustainable power generation Debani Prasad Mishra; Sarita Samal; Rohit Kumar; Arun Kumar Sahoo; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp365-373

Abstract

To improve sustainability in power generation, this study presents a thorough data-driven method for maximizing renewable energy sources. It employs measures like capacity utilization factor (CUF) and efficiency to evaluate the performance of solar and wind energy using historical weather and energy-generating data. The study offers practical suggestions for improving renewable energy systems, such as weather-energy correlation analysis and machine learning-based forecasting models. In addition, a comparative analysis is carried out to ascertain which energy source is better, and useful real-world data is provided, including a summary of all India’s total renewable energy generation (excluding large hydro) for June 2023 and a performance comparison year over year. A useful, data-driven approach for enhancing renewable energy is provided by this work, which advances the topic of sustainable energy.
Optimizing solar energy forecasting and site adjustment with machine learning techniques Debani Prasad Mishra; Jayanta Kumar Sahu; Soubhagya Ranjan Nayak; Anurag Panda; Priyanshu Paramjit Dash; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp384-392

Abstract

Estimation of solar radiation is a key task in optimizing the operation of power systems incorporating high levels of photovoltaic (PV) generation. This paper discusses the application of machine learning techniques, namely extreme gradient boosting (XGBT) and random forest (RF), to improve accuracy in the forecasting of solar radiation while adapting for different sites. Utilizing datasets such as meteorological and solar radiation data, the suggested models demonstrate the enhancement of forecasting accuracy by 39% from traditionally applied statistical practices. Along with this, this study also encompasses how endogenous and exogenous factors could be involved in better predictions of solar energy availability. From our findings, XGBT, as well as other machine learning techniques, do enjoy superior performance levels when it comes to the forecasting of solar radiation, which in turn promotes efficient management and potential adaptation of solar energy systems. This study demonstrates how this last generation of algorithms could be applied to noticeably improve the efficiency of solar power forecasting and thereby contribute to more sustainable and reliable energy systems as a byproduct of that.
Co-Authors Abhisek Sahoo Aditya Jasrotia Aditya Kumar Aishwarya Priyadarshini Amba Subhadarshini Nayak Anurag Panda Arun Kumar Sahoo Asutosh Samal Atman Panigrahi Jayanta Kumar Sahu Jayanta Kumar Sahu Kaushiki Agrawal Kshirod Kumar Rout Lakshay Bhardwaj Lingam Yashwanth Manas Ranjan Sahu Mandakurit Nivas Munavath Pavan Kalyan Padarabinda Palai Pavan Kumar Peesodi Uday Piyushjeet Piyushjeet Piyuskant Das Pranay Kumar Panda Pratyush Gupta Priyanshu Paramjit Dash Raj Alpha Swain Rakesh Kumar Lenka Rakesh Kumar Yadav Ramakanta Mohanty Rambilli Krishna Prasad Rao Naidu Rishabh Vishnoi Rohit Kumar Rohit Kumar Swain Rudra Narayan Senapati Rudranarayan Senapati S. S. Saswat Sanhita Mishra Sanhita Mishra Sarita Samal Shiv Grover Shivam Tyagi Sivkumar Mishra Smrutisikha Jena Somnath Banerjee Sonna Murari Soubhagya Ranjan Nayak Subhankar Dash Subhrajit Jena Suman Patra Surender Reddy Salkuti Truptasha Tripathy