In the present paper, Artificial Neural Network has been adopted to forecast the maximum and minimum temperature monsoon months. The temperature of June, July, August has been predicted with the help of January to May temperature. In both the cases, maximum and minimum temperature is greatly predicted in the month of August. In the largest part of the cases, prediction error lies below 5%.In formulating the ANN-based predictive model, three-layer network has been constructed. The data published by Indian Institute of Tropical Meteorology (http://www.tropmet.res.in) are explored to develop the predictive model. The analysis is found to produce a forecast with small prediction error.

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Significant technical progress is currently ongoing in the field of energy harvesting technologies. This paper presents a study of both photovoltaic (PV) and thermo-electric energy harvesting techniques in the context of mobile phone applications. The paper includes performance analysis of a macro scale PV module and a thermo-electric generator (investigated under different conditions), together with a single diode electrical equivalent circuit model of the solar PV developed using MATLAB/Simulink. Additionally experimental results of the PV module are verified via simulation. To complete the study a PV module is integrated with a DC/DC adjustable converter and solar charge controller (3A CMTP02) to charge a smart mobile phone in order to assimilate charging capacity and feasibility of macro scale energy harvesting devices.

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The maximum power point tracking (MPPT) algorithm has become an integral part of many charge controllers that are used in photovoltaic (PV) systems. Most of the existing algorithms have a compromise among simplicity, tracking speed, ability to track accurately, and cost. In this work, a novel “straight-line approximation based Maximum Power Point (MPP) finding algorithm” is proposed where the intersections of two linear lines have been utilized to find the MPP, and investigated for its effectiveness in tracking maximum power points in case of rapidly changing weather conditions along with tracking speed using standard irradiance and temperature curves for validation. In comparison with a conventional Perturb and Observe (P&O) method, the Proposed method takes fewer iterations and also, it can precisely track the MPP s even in a rapidly varying weather condition with minimal deviation. The Proposed algorithm is also compared with P&O algorithm in terms of accuracy in duty cycle and efficiency. The results show that the errors in duty cycle and power extraction are much smaller than the conventional P&O algorithm.

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In this study, the optimal conditions for renewable sources, Solar and Wind generators, have been determined for irrigation system in Sandwip, St. Martin and Kutubdia of Bangladesh by conducting a linear programming graphical analysis tool. For all of these three regions mentioned above, the optimum system is found to be solar-only system for irrigation based on the load conditions and meteorological data. Finally, the study shows the amount of energy the optimum PV generator has produced in a year with the surplus energy after satisfying the load demand.