Ajit Muzumdar
@nitgoa.ac.in
Scopus Publications
Scopus Publications
Ajit Muzumdar, Chirag Modi, and C. Vyjayanthi
Elsevier BV
Ajit A. Muzumdar, Chirag N. Modi, Madhu G. M, and Chintamani Vyjayanthi
Institute of Electrical and Electronics Engineers (IEEE)
The consumer’s short-term load forecasting plays an essential role in microgrid energy distribution. However, the load forecasting at consumer level is more challenging than at substation level due to high volatility and uncertainty in energy consumption. In literature, many machine learning-based forecasting models have been explored. However, there is a need of developing a robust and accurate model to handle highly inconsistent energy consumption. In this article, we propose a robust and accurate model for consumer’s short-term load forecasting, which uses feasible techniques such as random forest, support vector regressor, and long short-term memory as base predictors to handle varying traits of energy consumption. For the final decision-making on forecasting result of these predictors, it assigns weights to each predictor dynamically as per the forecasting efficacy. The proposed model is tested on different consumer’s varying traits of energy consumption. The experimental results show that the proposed model achieves forecasting error reduction by 3.46 and 2.53 in terms of average RMSE and MAE, respectively, in comparison with the existing models. It is robust and accurate even in presence of highly volatile and uncertain load patterns, and thus, it can better fit for microgrid energy management.
Ajit Muzumdar, Chirag Modi, and C. Vyjayanthi
Elsevier BV
Pooja Yadav, Shubham Sharma, Ajit Muzumdar, Chirag Modi, and C. Vyjayanthi
IEEE
The traditional process of renting the house has several issues such as data security, immutability, less trust and high cost due to the involvement of third party, fraudulent agreement, payment delay and ambiguous contracts. To address these challenges, a blockchain with smart contracts can be an effective solution. This paper leverages the vital features of blockchain and smart contract for designing a trustworthy and secured house rental system. The proposed system involves off-chain and on-chain transactions on hyperledger blockchain. Off-chain transaction includes the rental contract creation between tenant and landlord based on their mutual agreement. On-chain transactions include the deposit and rent payment, digital key generation and contract dissolution, by considering the agreed terms and conditions in the contract. The functional and performance analysis of the proposed system is carried out by applying the different test cases. The proposed system fulfills the requirements of house rental process with high throughput (>92 tps) and affordable latency (<0.7 seconds).
Alok Jaiswal, Sheetal Chandel, Ajit Muzumdar, Chirag Modi, Madhu G. M., and C. Vyjayanthi
Springer Singapore
Ajit Muzumdar, Chirag Modi, Madhu G.M., and C. Vyjayanthi
Elsevier BV
Ajit Muzumdar, Chirag Modi, and C Vyjayanthi
IEEE
The conventional grid has experienced a transition towards smart grid with the advancements in metering infrastructure and increasing usage of renewable energy sources. In smart grid, the energy management system relies heavily on an accurate short-term load forecasting at regional level for an efficient planning and operations of grid.In this paper, we propose an efficient model for regional short-term load forecasting using machine learning techniques in parallel. This model uses feasible machine learning techniques such as support vector regressor (SVR) and random forest (RF) as base predictors. The forecasting results of RF and SVR are averaged to derive final outcome. The performance of the proposed model is validated using load data collected from different regions such as Goa, Maharashtra and Mumbai in India.
Alok Jaiswal, Sheetal Chandel, Ajit Muzumdar, Madhu G.M., Chirag Modi, and C. Vyjayanthi
IEEE
Blockchain has transformed business processes from centralized to decentralized. It can better help in making food grain supply chain fully decentralized with peer to peer (P2P) business by removing intermediaries, and thus reducing the overall cost at end user side with better returns to farmers. In this paper, we explore a blockchain technology and the required smart contracts for trustworthy and incentivized P2P trading of food grains. We propose different smart contracts such as food grain supply, bidding, trading and utilization, which are deployed on ethereum blockchain for the decentralized trading of food grains. We use Vickrey-Clarkegrove (Vickrey auction) method for achieving the incentivized trading for both farmers and end users. The proposed framework offers P2P trading, security of food grain data, data transparency, user's anonymity and incentives in the trading process. The performance of the proposed framework is evaluated and analyzed in terms of fulfilling the requirements of food grain supply management.
Ajit Muzumdar, Chirag N. Modi, G M Madhu, and C. Vyjayanthi
IEEE
Smart grid involves distributed energy resources (DERs) such as Solar PV, Wind, Battery generations, etc to meet the increasing demand of energy. However, due to the increasing demand of energy, there is need of generating more energy from the available resources to avoid the energy imbalance. Energy imbalance is the core reason behind poor power quality and energy outage problems, and therefore it is required to predict the energy demand and supply on a daily basis for better preparedness to avoid energy crisis. This leads to the energy imbalance classification problem. In literature, many machine learning techniques have been used to address this problem. However, it is required to perform the feasibility analysis of such techniques. In this paper, we have investigated well-known machine learning techniques for energy imbalance classification in smart grid. For the feasibility analysis of different machine techniques, we have collected the energy generation and consumption data from Maharashtra region, India and preprocessed these data using the principal component analysis (PCA) which can help in improving the classification accuracy. For energy imbalance classification, we have considered different machine learning techniques such as Naive bayesian, neural network, support vector machine, decision tree, random forest, bagging and boosting. We have evaluated the performance results in terms of accuracy, root mean square error and mean absolute error.
Sandip Jangir, Ajit Muzumdar, Alok Jaiswal, Chirag N. Modi, Sheetal Chandel, and C. Vyjayanthi
IEEE
Supply chain in industry is a flow of information and goods traversal through multiple suppliers such as raw material suppliers, manufacturers, distributors, retailers and end users. Supply chain management (SCM) helps in smooth monitoring and accounting of information and goods traversal among multiple users. However, the existing supply chain management processes lack in offering transparency of information, user's privacy, timely updates on demand peaks, improper tracing of information, quality management, deal repudiation and trust among users. To address these problems, we propose a novel framework for pharmaceutical supply chain management which uses an ethereum blockchain, a distributed ledger technology. We propose different smart contracts to manage pharmaceutical supply chain processes over an ethereum blockchain. This framework helps in achieving user privacy, transparency of the supplied drug, tracking of drugs, quality management, non-repudiation and demand supply management with the improved trust among the users. At the end, we analyze the proposed framework in terms of fulfilling the pharmaceutical supply chain management requirements.