Krishna Pavan Inala

Future-Focused Leadership for AI and Next Generation Educational Technologies

Rampelli Manojkumar, T. Aditya Sai Srinivas, Krishna Pavan Inala, Niteesha Sharma et autres

Higher education is experiencing a paradigm shift driven by artificial intelligence (AI), data analytics, and digital transformation. AI now permeates teaching, research, and institutional governance, enabling personalization, automation, and data-informed decision-making. However, its integration presents challenges related to ethics, data privacy, equity, …

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Integrated Planning and Management Strategies From Leadership to Faculty Stakeholders

Rampelli Manojkumar, T. Aditya Sai Srinivas, Krishna Pavan Inala, B Venkatesh et autres

This chapter examines integrated planning and management strategies in higher education, emphasizing the interconnected roles of leadership, management, faculty, and stakeholders in building future-ready institutions. It situates institutional planning within a rapidly changing context shaped by digital transformation, sustainability imperatives, and evolving …

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Next Generation Healthcare Systems Driven by Metaverse, Artificial Intelligence, and Federated Learning

Rampelli Manojkumar, T. Aditya Sai Srinivas, Ramesh Babu Pittala, Santhosh Kumar Veeramalla et autres

This chapter examines the emergence of next-generation healthcare systems shaped by the integrated adoption of immersive digital environments, artificial intelligence, and privacy-preserving learning paradigms. The purpose of the chapter is to provide a structured overview of how metaverse architectures, AI-enabled analytics, digital …

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Electric Vehicle Load Forecasting in Smart Grid Using CatBoost Machine Learning Algorithm

Krishna Pavan Inala, Riza Tazmeen, Sameera Begum, Sivvagoni. Preethi et autres

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Smart Grid Security Attack Detection Using XG Boosting Combined with Genetic Algorithm

Badavath Akhila, Krishna Pavan Inala, Rampelli Manojkumar

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Load forecasting for home energy management using light gradient boosting machine learning algorithm

Krishna Pavan Inala, Rampelli Manojkumar

The deployment of smart metres has revolutionised the smart grid ecosystem by enabling advanced monitoring and load prediction techniques, which benefit both electricity providers and consumers. These techniques utilise historical consumption data for short-term energy demand forecasting, supporting efficient energy management. Electricity …

Smart Energy Allocation for Electric Vehicles Using Predictive Modelling

Krishna Pavan Inala, K. Narayanan, Kongoti Srujana, Gowraiahgari Shravya et autres

Efficient energy allocation for electric vehicle (EV) charging is critical to optimize grid performance and enhance user satisfaction. In this research, we utilize historical EV charging data from urban networks to develop predictive models for smart energy allocation. By applying advanced machine …

Enhancing the Power Load Prediction using LSTM in a Smart Grid Scenario for Household Data

Krishna Pavan Inala, S. Harikrishna Etti, Jahnavi Karangula, Pranay Kashetty

The smart grid fuses modern technology with traditional infrastructure to enhance efficiency and sustainability. Accurate Load Forecasting (LF) is crucial for ensuring reliability and rising electricity demand, while integration of machine learning transforms energy and grid management. In this research real-time consumption …

Predicting energy consumption for smart buildings in a smart grid scenario using machine learning models

Krishna Pavan Inala, Nilotpal Chakraborty

Forecasting the consumption of energy in smart buildings within a smart grid scenario is crucial for optimizing energy use, reducing costs, and enhancing grid stability. Existing literature emphasizes the importance of accurate forecasting models, as energy consumption is determined by a variety …

Rule-Based Peak Shaving Using Battery Energy Storage with a Case Study Analysis

Rampelli Manojkumar, Krishna Pavan Inala

In recent times, energy management in low-voltage distribution networks has become increasingly important, driven by the need for energy efficiency, cost reductions, and alignment with global environmental goals. Peak shaving plays a key role in this framework, benefiting both electricity consumers and …

Optimised rule-based peak shaving and its impact on annual energy cost reduction

Rampelli Manojkumar, Krishna Pavan Inala

This paper presents an optimised rule-based peak-shaving method which is tested on an educational institute to know its impact on annual energy cost reduction. In recent years, energy management in low-voltage distribution systems has drawn more attention. Peak shaving is an important …

Optimal Load Prediction in a Smart Metering Network using Random Forest Algorithm

Krishna Pavan Inala, Kottu Krishna Teja, Kalakoti Archana

Smart meters are electronic devices used to measure and record real-time energy consumption. In this research, we utilized energy consumption data gathered from residential consumers in London, England. Our aim is to leverage various machine learning algorithms to establish connections between historical …