Faculty Publications

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Author: search.filters.author.Deka, P.C.Subject: search.filters.subject.accuracy assessment

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    Offshore wind power resource assessment using Oceansat-2 scatterometer data at a regional scale
    (Elsevier Ltd, 2016) Gadad, S.; Deka, P.C.
    In the offshore region the scarcity of in situ wind data in space proves to be a major setback for wind power potential assessments. Satellite data effectively overcomes this setback by providing continuous and total spatial coverage. The study intends to assess the offshore wind power resource of the Karnataka state, which is located on the west coast of India. Oceansat-2 scatterometer (OSCAT) wind data and GIS based methodology were adopted in the study. The OSCAT data accuracy was assessed using INCOIS Realtime All Weather Station (IRAWS) data. Wind speed maps at 10 m, 90 m and wind power density maps using OSCAT data were developed to understand the spatial distribution of winds over the study area. Bathymetric map was developed based on the available foundation types and demarking various exclusion zones to help in minimizing conflicts. The wind power generation capacity estimation performed using REpower 5 MW turbine, based on the water depth classes was found to be 9,091 MW in Monopile (0-35 m), 11,709 MW in Jacket (35-50 m), 23,689 MW in Advanced Jacket (50-100 m) and 117,681 MW in Floating (100-1000 m) foundation technology. In Indian scenario major thrust for wind farm development in Monopile region is required. Therefore as first phase of development, if 10% of the estimated potential in the region can be developed then, 116% of energy deficit for FY 2011-12 could be met. Also, up to 79% of the anticipated energy deficit for the FY 2014-15 of the Karnataka state could be achieved. © 2016 Elsevier Ltd.
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    Dew Point temperature estimation: Application of artificial intelligence model integrated with nature-inspired optimization algorithms
    (MDPI AG indexing@mdpi.com Postfach Basel CH-4005, 2019) Naganna, S.R.; Deka, P.C.; Ghorbani, M.A.; Biazar, S.M.; Al-Ansari, N.; Yaseen, Z.M.
    Dew point temperature (DPT) is known to fluctuate in space and time regardless of the climatic zone considered. The accurate estimation of the DPT is highly significant for various applications of hydro and agro-climatological researches. The current research investigated the hybridization of a multilayer perceptron (MLP) neural network with nature-inspired optimization algorithms (i.e., gravitational search (GSA) and firefly (FFA)) to model the DPT of two climatically contrasted (humid and semi-arid) regions in India. Daily time scale measured weather information, such as wet bulb temperature (WBT), vapor pressure (VP), relative humidity (RH), and dew point temperature, was used to build the proposed predictive models. The efficiencies of the proposed hybrid MLP networks (MLP-FFA and MLP-GSA) were authenticated against standard MLP tuned by a Levenberg-Marquardt back-propagation algorithm, extreme learning machine (ELM), and support vector machine (SVM) models. Statistical evaluation metrics such as Nash Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE) were used to validate the model efficiency. The proposed hybrid MLP models exhibited excellent estimation accuracy. The hybridization of MLP with nature-inspired optimization algorithms boosted the estimation accuracy that is clearly owing to the tuning robustness. In general, the applied methodology showed very convincing results for both inspected climate zones. © 2019 by the authors.