• Energy Research
• 7. Clean energy close

Efficient extraction of renewable energy from wind depends on the reliable estimation of wind characteristics and optimization of wind farm installation and operation conditions. There exists uncertainty in the prediction of wind energy tapping potential based on the variability in wind behavior. Thus the estimation of wind power density based on empirical models demand subsequent data processing to ensure accuracy and reliability in energy computations. Present study analyses the reliability of the ANN-based machine learning approach in predicting wind power density for five stations (Chennai, Coimbatore, Madurai, Salem, and Tirunelveli) in the state of Tamil Nadu, India using five different non-linear models. The selected models such as Convolutional Neural Network (CNN), Dense Neural Network (DNN), Recurrent Neural Network (RNN), Bidirectional Long Short Term Memory (LSTM) Network, and linear regression are employed for comparing the data for a period from Jan 1980 to May 2018. Based on the results, it was found that the performance of (1->Conv1D|2->LSTM|1-dense) is better than the other models in estimating wind power density with minimum error values (based on mean absolute error and root mean squared error).

An accurate estimate of wind resource assessment is essential for the identification of potential site for wind farm development. The hourly average wind speed measured at 50 m above ground level over a period of 39 years (1980-2018) from 25 locations in Tamil Nadu, India have been used in this study. The annual and seasonal wind speed trends are analyzed using linear and Mann-Kendall statistical methods. The annual energy yield, and net capacity factor are obtained for the chosen wind turbine with 2 Mega Watt rated power. As per the linear trend analysis, Chennai and Kanchipuram possess a significantly decreasing trend, while Nagercoil, Thoothukudi, and Tirunelveli show an increasing trend. Mann-Kendall trend analysis shows that cities located in the southern peninsula and in the vicinity of the coastal regions have significant potential for wind energy development. Moreover, a majority of the cities show an increasing trend in the autumn season due to the influence of the retreating monsoons which is accompanied with heavy winds. The mean wind follows an oscillating pattern throughout the year at all the locations. Based on the net annual energy output, Nagercoil, Thoothukudi and Nagapattinam are found to be the most suitable locations for wind power deployment in Tamil Nadu, followed by Cuddalore, Kumbakonam, Thanjavur and Tirunelveli.

Globally, the wind power capacities are growing every passing year, which is an indicative of social and commercial acceptance of this technology by a larger section of the populations. In Indian perspective, the wind power capacities are also increasing with annual additions of new capacities and most of the development work is taking place in the southern part and that too in Tamil Nadu state. Research work in the area of accurate wind power assessment is being conducted to optimize the utilization of wind power and at the same time efforts are being exerted to enhance the operation and maintenance capabilities of the local skilled and semi-skilled work force. This study utilizes 38 years of hourly mean wind speed data from seven locations for providing the accurate wind power assessment and understanding the longitudinal behavior of its characteristics. The wind speed is found to be increasing with decreasing latitudes and having lesser variation in wind direction fluctuations, simply means conversing wind direction to narrower bands. Kanyakumari is identified as the most probable wind power deployment site with annual energy yield of 227.55 MWh and capacity factor of 34% followed by Vedaranyam, and Thoothukudi, as second and third priority sites with respective annual yields of 223.36 MWh and 218.73 MWh.

The objective of this work is to understand the fluctuating nature of wind speed characteristics on different time scales and to find the long-term annual trends of wind speed at different locations in South Africa. The hourly average mean wind speed values over a period of 20 years are used to achieve the set objective. Wind speed frequency, directional availability of maximum mean wind speed, total energy, annual energy yield and plant capacity factors are determined for seven locations situated both inland and along the coast of South Africa. The highest mean wind speed (6.01 m/s) is obtained in Port Elizabeth and the lowest mean wind speed (3.86 m/s) is obtained in Bloemfontein. Wind speed increased with increasing latitudes at coastal sites (Cape Town, Durban, East London and Port Elizabeth), while the reverse trend was observed at inland locations (Bloemfontein, Johannesburg and Pretoria). Noticeable annual changes and relative wind speed values are found at coastal locations compared to inland sites. The energy pattern factor, also known as the cube factor, varied between a minimum of 1.489 in Pretoria and a maximum of 1.858 in Cape Town. Higher energy pattern factor (EPF) values correspond to sites with fair to good wind power potential. Finally, Cape Town, East London and Port Elizabeth are found to be good sites for wind power deployments based on the wind speed and power characteristics presented in this study.

Abstract Wind energy is a potent yet freely available renewable energy. It is essential to estimate the wind speed (WS)precisely to makeaprecise estimation of wind power at wind power generating stations.Generally, the WS data is non-stationary. Wavelets have the potential to deal with the non-stationarilyindatasets. On the other hand, the prediction ability of primal least square support vector regression (PLSTSVR) has never been tested to best of our knowledge for WS prediction. Hence, in this work, wavelet kernel-based LSTSVR models are proposed for WS prediction. They are Morlet wavelet kernel LSTSVR and Mexican Hat wavelet kernel LSTSVR.HourlyWS data are collected from four different stations namely Chennai, Madurai, Salem and Tirunelveli in Tamil Nadu, India. The performance of the proposed models isevaluated using root mean square, mean absolute, symmetric mean absolute percentage, mean absolute scaled error and R2. The results of the proposed models are compared with twin support vector regression (TSVR), PLSTSVR and large-margin distribution machine-based regression (LDMR). Based on the results of the performance indicators, the performance of the proposed models is better when compared to other models.

Abstract The demand for sustainable building materials is increasing day-by-day pertaining to the challenges in meeting the cost, reliability and climate adaptability. In addition to the structural requirements, improvements in the building technology are also trending towards eco-friendly, comfortable and economic housing solutions. The present study deals with the usage of two industrial waste materials (bagasse ash and granite powder) to prepare lightweight and heat resistant concrete panels (M25 grade). The replacement of aggregates was accomplished sequentially by varying the mix proportioning (10%, 15%, 20% and 25%) using granite powder and bagasse ash balls (~10mm dia.). In addition to the weight reduction, coconut fibres and paraffin were also applied in intermittent layers to enhance thermal insulation properties. The temperature profiles were monitored using thermocouple sensors fitted onto the panel surfaces. The physical, mechanical and thermal properties of the casted panels were studied after 28 days of curing. A comparative study was made for solid and hollow light panels in terms of strength and weight. The results indicated that significant temperature reduction is achieved between the surfaces by using the phase changing materials (3%) while the compressive strength of the hollow lightweight panel is satisfactory (23.48 N/mm2). Hence, the prepared concrete panels can be suitably employed in building construction where thermal comfort and reduced weight are in demand.

To combat the adverse environmental effects of fossil fuel burning for power generation and to conserve it for strategic use, new, clean, and renewable energy sources are being utilized for power generation. The study presents techno-economic analysis of a grid-connected solar photovoltaic (PV) power plant to partially meet the energy consumption of the people of Kuttiady village in Kerala, India. The proposed 2315.5 kW installed capacity PV is found to be feasible for the village and can produce 3878.3 MWh of energy annually while the demand is 4044.86 MWh at a plant capacity factor of 19.1% and cost of energy of 290.73 $/MWh. The performance of the proposed PV plant measured in terms of final yield (4.59 h), reference yield (5.64 h), and performance ratio (82%) is compatible and even higher with many such plants in India and other countries. Economic sensitivity analysis is also performed by varying the interest, discount, and inflation rates to check their effect on cost of energy, benefit cost ratio, and payback period. As the interest and discount rates decrease, the cost of energy and payback period also decreases while benefit cost ratio increases. The proposed plant can help in avoiding around 785 tons of greenhouse gases entering the local atmosphere of the Kuttiady village.

Wind energy is one of the abundant, cheap and fast-growing renewable energy sources whose intensive extraction potential is still in immature stage in India. This study aims at the determination and evaluation of wind energy potential of three cities located at different elevations in the state of Tamil Nadu, India. The historical records of wind speed, direction, temperature and pressure were collected for three South Indian cities, namely Chennai, Erode and Coimbatore over a period of 38 years (1980-2017). The mean wind power density was observed to be highest at Chennai (129 W/m2) and lowest at Erode (76 W/m2) and the corresponding mean energy content was highest for Chennai (1129 kWh/m2/year) and lowest at Erode (666 kWh/m2/year). Considering the events of high energy-carrying winds at Chennai, Erode and Coimbatore, maximum wind power density were estimated to be 185 W/m2, 190 W/m2 and 234 W/m2, respectively. The annual average net energy yield and annual average net capacity factor were selected as the representative parameters for expressing strategic wind energy potential at geographically distinct locations having significant variation in wind speed distribution. Based on the analysis, Chennai is found to be the most suitable site for wind energy production followed by Coimbatore and Erode.