Sathyajith Mathew

• Jones, Johnson Foyken; Raj, Veena; Abas, Pg Emeroylariffion; Petra, M. I.; Sivan, Dawn & Kumar, K. Satheesh [Show all 8 contributors for this article] (2025). Application of artificial intelligence for asset integrity management of offshore oil and gas pipelines. Life Cycle Reliability and Safety Engineering. ISSN 2520-1352. 14(4), p. 503–530. doi: 10.1007/s41872-025-00353-2. Full text in Research Archive Show summary

  The assets and infrastructure of offshore oil and gas platforms, such as pipelines, operate in harsh environments and often require frequent maintenance for sustained operation. The conventional asset management protocols, such as pigging, aerial and surface inspection, and ultrasonic testing, are resource-intensive and often reactive, leading to high operational costs and unplanned downtime. In this context, the capabilities of artificial intelligence (AI) can be effectively leveraged to manage these assets efficiently and economically. This paper investigates the recent advances in AI-driven asset management to enhance the reliability, safety, and cost-effectiveness of offshore oil and gas pipeline asset management. The study begins with mapping recent research on AI applications in oil and gas pipeline management using citational network analysis. Eight key thematic clusters were identified, each highlighting specific research areas and future directions. Based on these insights, a structured AI-based asset integrity management system is proposed for oil and gas infrastructure, including pipelines, accounting for practical implementation considerations such as system integration, data reliability, and regional constraints. The system is structured into three phases: operational asset integrity management, asset life extension, and AI-driven applications. This approach integrates both qualitative and quantitative methods, utilizing real-time data analytics, machine learning, and predictive modeling. The proposed protocols are designed to be a practical and affordable roadmap for achieving continuous, reliable and safe integrity management supported by real-time analytical and economic justification. While the framework offers broader applicability, regional environmental factors and data variability may require adjustments to suit specific contexts.

• Femin, V.; Veena, R.; Petra, M.I. & Mathew, Sathyajith (2025). Comparative analysis of different PV technologies under the tropical environments. Scientific Reports. 15(1). doi: 10.1038/s41598-025-99958-x. Full text in Research Archive Show summary

  In this paper, six different types of solar PV technologies are compared in terms of their performances under tropical conditions, using three years of performance data from a 1.2 MW experimental solar farm. The technologies considered include single-crystalline silicon, polycrystalline silicon, microcrystalline silicon, amorphous silicon, copper indium selenium (CIS), and hetero-junction with intrinsic thin layer (HIT). The field performances of these cells were initially assessed using standard performance indices such as Array Yield, Reference Yield, Capture Loss, Performance Ratio, and Efficiency Ratio. Among the technologies studied, amorphous silicon and HIT-based systems demonstrated better performance, showing higher Performance and Efficiency Ratios, along with lower capture losses. This study also modelled the fluctuations in power production from these panels. Under probabilistic modeling, the ramping behavior of the systems was characterized using the Generalized Logistic Distribution. Based on this analysis, CIS PV systems were found to have minimum power ramps, where as the HIT based systems showed the highest power fluctuations. To predict minute-wise and hourly ramping of the PV systems under varying levels of solar insolation, machine learning methods based on Artificial Neural Networks (ANN), Support Vector Machines (SVM), and k-Nearest Neighbors (kNN) were developed. With a Normalized Root Mean Square Error (NRMSE) of over 96%, these models demonstrated high accuracy in capturing the ramping characteristics of the studied PV systems. The results of this study offer valuable insights into the performance of different PV systems under tropical regions, which can be used in efficiently designing and managing solar PV projects.

• Hamdan, Ateeb; Panda, Sugato; Jain, Mayur Shirish; Raj, Veena & Mathew, Sathyajith (2025). Assessing municipal solid waste in Indian smart cities: A path towards Waste-to-Energy. Heliyon. 11(6). doi: 10.1016/j.heliyon.2025.e42770. Full text in Research Archive Show summary

  Urbanization, industrialization, unplanned urban expansion, and rural migration have significantly disrupted waste generation dynamics and characteristics, reducing the efficiency of municipal solid waste (MSW) management frameworks. Moreover, it has posed a substantial challenge to India's smart city initiative, which aims to develop sustainable cities. Additionally, the increasing energy demands in urban centers, driven by lifestyle habits, have exacerbated the depletion of non-renewable energy sources, highlighting the imperative need for cleaner energy sources and a thorough assessment of their adoption potential. This study comprehensively examines the current status of the functional elements of MSW, including its financial and administrative structures. It also maps its current waste management practices in the demographic space of 10 Indian smart cities. The study collected resources from secondary literatures, government and local authorities databases, and reports from key stakeholders. The paper provides recommendations and insights on the feasibility, challenges, and potential of adopting waste-to-energy (WtE) systems in the Indian context. The findings of this paper serve as a foundation for further optimization studies of MSW from a WtE perspective, aiding local authorities in identifying feasible pathways for integrating WtE technologies. This review offers a benchmark for municipalities to facilitate a smoother transition towards cleaner and energy-secured smart cities.

• Mian, Haris Hameed; Siddiqui, Muhammad Salman; Keprate, Arvind & Mathew, Sathyajith (2024). Predictive Modeling of Semi-Submersible Floater Motion Using Bi-LSTM Model. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 2875. doi: 10.1088/1742-6596/2875/1/012029. Full text in Research Archive Show summary

  Floating offshore wind turbines (FOWTs) are emerging as a promising renewable energy solution, tapping into the vast wind resources in deep-sea locations. Accurate predictions of heave, pitch, sway, roll, yaw, and surge are essential to ensuring the structural integrity and power generation efficiency of FOWTs. Traditional methods relying on potential flow theory to predict FOWTs’ hydrodynamic responses involve simplifications that may not capture real-world complexities. Although computational fluid dynamics (CFD) simulations offer accuracy, they are computationally expensive. This study explores the use of Bi-directional long-short-term memory (Bi-LSTM) neural networks as an alternative to address computational challenges in predicting FOWTs’ hydrodynamic responses. These networks excel at handling time series data, capturing temporal correlations crucial for understanding FOWT floater motion dynamics. They also effectively capture bidirectional dependencies, enhancing their suitability for this application. The model considers various input parameters, including wave amplitudes, periods, steepness, directions, and floater design characteristics such as stiffness, pre-tension, and the free length of mooring lines. The Bi-LSTM model is trained using a subset of the CFD dataset generated for the scaled OC5 semi-submersible platform with mooring, reserving the remainder for validation and testing. The comparison between simulated and predicted motion demonstrated a strong correlation in surge but noticeable discrepancies in heave and pitch. FFT analysis highlighted consistent frequency components but variations in magnitude, suggesting areas for model sensitivity improvement and further investigation into wave interaction dynamics. The correlation coefficients revealed a strong positive correlation for the surge motion, while the heave and pitch motions showed slightly lower correlation coefficients, suggesting better performance in predicting the surge.

• Pillai, Vipin Raj Rajendran; Valsala, Rohit Rajasekharan Nair; Raj, Veena; Petra, Muhammed Iskandar; Nair, Satheesh Kumar Krishnan & Mathew, Sathyajith (2023). Exploring the Potential of Microgrids in the Effective Utilisation of Renewable Energy: A Comprehensive Analysis of Evolving Themes and Future Priorities Using Main Path Analysis. Designs. 7(3). doi: 10.3390/designs7030058. Full text in Research Archive Show summary

  Microgrids are energy systems that can operate independently or in conjunction with the main electricity grid. Their purpose is to link different energy sources, enhance customer participation in energy markets, and improve energy system efficiency and flexibility. However, regulatory, technical, and financial obstacles hinder their deployment. To comprehend the current state of the field, this study utilized citation network analysis (CNA) methodology to examine over 1500 scholarly publications on microgrid research and development (R&D). The study employed modularity-based clustering analysis, which identified seven distinct research clusters, each related to a specific area of study. Cluster 1, focused on control strategies for microgrids, had the highest proportion of publications (23%) and the maximum citation link count (151), while Cluster 4, which examined microgrid stability, had the lowest proportion of papers (10%). On average, each publication within each cluster had four citation links. The citation network of microgrid research was partitioned using cluster analysis, which aided in identifying the main evolutionary paths of each subfield. This allowed for the precise tracing of their evolution, ultimately pinpointing emerging fronts and challenges. The identification of key pathways led to the discovery of significant studies and emerging patterns, highlighting research priorities in the field of microgrids. The study also revealed several research gaps and concerns, such as the need for further investigation into technical and economic feasibility, legislation, and standardization of microgrid technology. Overall, this study provides a comprehensive understanding of the evolution of microgrid research and identifies potential directions for future research.

• Yakoub, Ghali; Mathew, Sathyajith & Leal, J B (2023). Direct and indirect short-term aggregated turbine- and farm-level wind power forecasts integrating several NWP sources. Heliyon. 9(11). doi: 10.1016/j.heliyon.2023.e21479. Full text in Research Archive Show summary

  The wind power sector is experiencing rapid growth, which creates new challenges for its electricity grid integration. Accurate wind power forecasting (WPF) is crucial for trading, balancing, and dispatching wind energy. In this paper, we examine the use of aggregated turbine- and farmlevel WPFs in the Nordic energy market. The turbine-level WPFs were retrieved from a previous study, while the farm-level WPFs were developed using the same methodology, incorporating inputs from three different numerical weather predictions (NWPs) and implementing both direct and indirect forecasting approaches. In the indirect WPF approach, we explore the impact of using wind direction as an input for the wind farm-level power performance model. The different WPFs are combined into one using weights related to up-to-date forecast errors. An automated and optimized machine-learning pipeline using data from a Norwegian wind farm is used to implement the proposed forecasting methods. The indirect approach, that uses the wind-downscaling model, improves the wind speed forecast accuracy compared to raw forecasts from the relevant NWPs. Additionally, we observed that the farm-level downscaling model exhibited lower error than those developed at the turbine level. The combined use of multiple NWP sources reduced forecasting errors by 8 %–30 % for direct and indirect WPFs, respectively. Direct and indirect forecasting methods present similar performance. Finally, the aggregated turbine-level improved WPF accuracy by 10 % and 15 % for RMSE and MAE, respectively, compared to farm-level WPF.

• Kunjuramakurup, Lijin; Sulthan, Sheik Mohammed; Ponparakkal, Muhammed Shanir; Raj, Veena & Mathew, Sathyajith (2023). A High-Power Solar PV-fed TISO DC-DC Converter for Electric Vehicle Charging Applications. Energies. 16(5). doi: 10.3390/en16052186. Full text in Research Archive Show summary

  In this paper, a two-input, single-output (TISO) DC-DC converter for electric vehicle charging applications with solar photovoltaic (PV) as one of the sources is discussed. A novel, simple, and effective control strategy with maximum power point tracking (MPPT) to maximize power from PV while maintaining a constant bus voltage is proposed. A 100 V, 100 W TISO DC-DC converter is designed and comprehensive simulations are conducted under different conditions. The system characteristics are validated by comparing the results with a conventional P&O algorithm and multiple step size P&O algorithm. The proposed control strategy can precisely generate control signals to track MPPT while maintaining the bus voltage by controlling the output of a fixed DC source according to the changes in PV generation. The overall efficiency of the proposed approach is 98.67%, and the average output voltage is 99.09 V under selected conditions. The overall average output voltage ripple is 0.22% with the proposed approach, while it is 2.91% and 1.55% with the conventional P&O and multiple step size P&O MPPT techniques, respectively. Further, a 500 V, 17 kW high-power converter is designed and a simulation is carried out. The high-power converter provides an average output voltage of 496.92 V with an overall efficiency of 98.72%, and the average output voltage ripple is 0.16%. The results obtained from the simulation under the selected conditions lead to the conclusion that the converter has better efficiency and less variation in the output voltage at higher power levels with the proposed control technique.

• Veena, Raj; Dotse, Sam-Quarcoo; Mathew, Sathyajith; Petra, M. I. & Yassin, Hayati (2023). Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems. Energies. 16(2). doi: 10.3390/en16020671. Full text in Research Archive Show summary

  In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year’s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant. After cleaning the data for errors and outliers, the model features were chosen on the basis of principal component analysis. Accuracies of the developed models were tested and compared with the performance of models based on other supervised learning algorithms, such as k-nearest neighbour and support vector machines. Though the accuracies of the models varied with the type of PV systems, in general, the machine learned models developed under the study could perform well in predicting the power output from different solar PV technologies under varying working environments. For example, the average root mean square error of the models based on the gradient boosting machines, random forest, k-nearest neighbour, and support vector machines are 17.59 kW, 17.14 kW, 18.74 kW, and 16.91 kW, respectively. Corresponding averages of mean absolute errors are 8.28 kW, 7.88 kW, 14.45 kW, and 6.89 kW. Comparing the different modelling methods, the decision-tree-based ensembled algorithms and support vector machine models outperformed the approach based on the k-nearest neighbour method. With these high accuracies and lower computational costs compared with the deep learning approaches, the proposed ensembled models could be good options for PV performance predictions used in real and near-real-time applications.

• Yakoub, Ghali; Mathew, Sathyajith & Leal, J B (2022). Power production forecast for distributed wind energy systems using support vector regression. Energy Science & Engineering. ISSN 2050-0505. 10(12), p. 1–12. doi: 10.1002/ese3.1295. Full text in Research Archive Show summary

  Due to the inherent intermittency in wind power production, reliable short-term wind power production forecasting has become essential for the efficient grid and market integration of wind energy. The current wind power production forecasting schemes are predominantly developed for wind farms. With the rapid growth in the microgrid sector and the increasing number of wind turbines integrated with these local grids, power production forecasting schemes are becoming essential for distributed wind energy systems as well. This paper proposes a power production forecasting scheme developed explicitly for distributed wind energy projects. The proposed system integrates two submodels based on support vector regression: one for downscaling the wind speed predictions to the hub coordinates of the turbine and the other for predicting the site-specific performance of the turbine under this wind condition. The forecasting horizons considered are the hour ahead (t + 1) and the day ahead (t + 36), which align with the Nord pool's energy market requirements. For the day-ahead scheme, a multistep recursive approach is adopted. The accuracy and adaptability of the proposed forecasting scheme are demonstrated in the case of a distributed wind turbine.

• Yakoub, Ghali; Mathew, Sathyajith & Leal, João Gouveia Aparício Bento (2022). Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models. Energy. ISSN 0360-5442. 263(D). doi: 10.1016/j.energy.2022.125893. Full text in Research Archive Show summary

  Reliable wind power forecasting is essential for profitably trading wind energy in the electricity market and efficiently integrating wind-generated electricity into the power grids. In this paper, we propose short- and medium-term wind power forecasting systems targeted to the Nordic energy market, which integrate inputs on the wind flow conditions from three numerical weather prediction sources. A point forecasting scheme is adopted, which forecasts the power at the individual turbine level. Both direct and indirect forecasting approaches are considered and compared. An automated machine-learning pipeline, built and optimized using genetic programming, is implemented for developing the proposed forecasting models. The turbine level power forecasts using different approaches are then combined into a single forecast using a weighting method based on recent forecast errors. These are then aggregated for the wind farm level power estimates. The proposed forecasting schemes are implemented with data from a Norwegian wind farm. We found that in both the direct and indirect forecasting approaches, the forecasting errors could be reduced between 8% and 22%, while inputs from several NWP sources are used together. The wind downscaling model, which is used in the indirect forecasting approach, could significantly contribute to the model's accuracy. The performance of both the direct and indirect forecasting schemes is comparable for the studied wind farm.

• Mathew, Sathyajith (2020). Sustainable energy towards air pollution and climate change mitigation. Journal of Environmental Management (JEM). ISSN 0301-4797. 260(109978), p. 1–11. doi: 10.1016/j.jenvman.2019.109978. Full text in Research Archive Show summary

  This is an evidence from a high-income economy in Southeast Asia and a support for scientific planning of the energy sector in ensuring air pollution and climate change mitigation. A comparative analysis of the energy options for electricity generation in the nation was made considering availability, cost and greenhouse gases emission – CO2, N2O and CH4, using a two-stage method comprising multi-objective optimization and TOPSIS. The renewable (RE) and non-renewable energy (NRE) options available were assessed through the lifecycle approach to determine the lifecycle greenhouse gas emission (LCGHG) and levelized cost of energy (LCOE) per MWh of electricity. Considering historical electricity consumption, annual GDP and population growth from 1965, energy consumption for the year 2035 was forecasted using support vector machine regressor in Weka. Future plans in energy diversification pathways were examined through various scenario multi-objective optimizations with a constraint on resource availability and energy target using genetic algorithm in MATLAB. The outputs were ranked using TOPSIS method. Results showed that greenhouse gases emission could be reduced by 10.3 percent compared to business as usual scenario while the energy mix could attain 10 percent renewable energy in the grid at a relatively lower generation cost.

• Femin, V.; Petra, I.; Mathew, Sathyajith & Hazra, J (2020). Econo- Environmental Dispatch Solutions for Power Systems Integrated with Renewable Energy Resources, 2020 International Conference and Utility Exhibition on Energy, Environment and Climate Change (ICUE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728183343. doi: 10.1109/ICUE49301.2020.9307047. Full text in Research Archive
• Veena, R.; Mathew, Sathyajith & Petra, M.I. (2020). Artificially intelligent models for the site-specific performance of wind turbines. International Journal of Energy and Environmental Engineering (IJEEE). ISSN 2008-9163. 11, p. 289–297. doi: 10.1007/s40095-020-00352-2. Full text in Research Archive
• Mathew, Sathyajith (2020). Wake Induced Power Losses in Wind Farms. International Journal of Engineering and Advanced Technology. 9(3), p. 2175–2180. doi: 10.35940/ijeat.C5611.029320. Full text in Research Archive
• Yakoub, Ghali; Mathew, Sathyajith & Leal, J B (2020). Downscaling and improving the wind forecasts from NWP for wind energy applications using support vector regression. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 1618. doi: 10.1088/1742-6596/1618/6/062034. Full text in Research Archive
• Veena, R.; Manuel, S. M.; Mathew, Sathyajith & Petra, I. (2020). Parametric Models for Predicting the Performance of Wind Turbines. Materials Today: Proceedings. 24(3). doi: 10.1016/j.matpr.2020.03.604. Full text in Research Archive
• Mathew, Sathyajith (2018). EnergyPlus models for the benchmarking of residential buildings in Brunei Darussalam. Energy and Buildings. ISSN 0378-7788. doi: 10.1016/j.enbuild.2016.03.039. Full text in Research Archive
• Mathew, Sathyajith (2015). Numerical simulation of flow over an airfoil for small wind turbines using the γ−Reθ model. International Journal of Energy and Environmental Engineering (IJEEE). ISSN 2008-9163. doi: 10.1007/s40095-015-0188-7. Full text in Research Archive
• Mathew, Sathyajith (2014). A Novel Low Reynolds Number Airfoil Design for Small Horizontal Axis Wind Turbines. Wind Engineering : The International Journal of Wind Power. ISSN 0309-524X. doi: 10.1260/0309-524x.38.4.377. Full text in Research Archive
• Mathew, Sathyajith (2003). A High-Power Solar PV-fed TISO DC-DC Converter for Electric Vehicle Charging Applications. Energies. Full text in Research Archive

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• Mathew, Sathyajith (2011). Advances in Wind Energy Conversion Technology. Springer. ISBN 9783540882589. 216 p. Full text in Research Archive
• Mathew, Sathyajith (2006). Wind Energy. Springer. ISBN 9783540309062. 252 p. Full text in Research Archive

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• Mian, Haris Hameed; Siddiqui, Muhammad Salman; Keprate, Arvind & Mathew, Sathyajith (2024). Neural network-based motion prtediction of semi-submercible floating platform for offshore wind. Full text in Research Archive
• Mathew, Sathyajith (2023). An Introduction to Wind Power Forecasting. Full text in Research Archive
• Mathew, Sathyajith (2023). Wind power forecasting. Full text in Research Archive
• Mathew, Sathyajith (2020). Econo-Environmental Dispatch Solutions for Power Systems Integrated with Renewable Energy Resources. Full text in Research Archive
• Mathew, Sathyajith (2020). Wind Energy Conversion. Full text in Research Archive
• Mathew, Sathyajith (2019). Basics of Wind Energy Conversion. Full text in Research Archive

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