The potential of wind energy via an intelligent IoT-oriented assessment

In contemporary times, renewable energy reliability has been an important field of research that is combined with the Internet of Things (IoT) including the opportunities for improving and challenging the work. Wind energy harvesting in IoT (WHIoT)-based framework is investigated considering the associated potential in the historical city of Bam is proposed in this paper. Weibull distribution function, wind power, and energy density were computed for three heights of 40, 60, and 80 m. The results at 80 m show that the maximum monthly wind speed average of 8.48 m/s occurred in July while the minimum value was observed in December with a value of 3.92 m/s. It was demonstrated that during the summer season power density and energy density are at peak values. This is an advantageous observation for Bam city with higher demand for energy during hot summer. The performance of six different wind turbines was assessed in terms of energy production and capacity factor. Finally, an economic assessment was performed to investigate the suitability of Bam city for the installation of small-scale wind turbines.

[1]  Ali Mostafaeipour,et al.  Economic feasibility of developing wind turbines in Aligoodarz, Iran , 2013 .

[2]  Farhan Ullah,et al.  A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis , 2019, Energies.

[3]  Macarena Espinilla,et al.  A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy , 2020, Sensors.

[4]  Ali Mostafaeipour,et al.  Feasibility study of harnessing wind energy for turbine installation in province of Yazd in Iran , 2010 .

[5]  V. Morgan.  Statistical distributions of wind parameters at Sydney, Australia , 1995 .

[6]  Ali Mostafaeipour,et al.  Wind energy feasibility study for city of Shahrbabak in Iran , 2011 .

[7]  A. Teimourian,et al.  Assessment of wind energy potential in the southeastern province of Iran , 2019, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

[8]  Behnam Mohammadi-Ivatloo,et al.  Long-Term Wind Power Forecasting Using Tree-Based Learning Algorithms , 2020, IEEE Access.

[9]  A. Mostafaeipour,et al.  Harnessing wind energy at Manjil area located in north of Iran , 2008 .

[10]  John L. Zhou,et al.  Evaluation of wind resource potential using statistical analysis of probability density functions in New South Wales, Australia , 2020 .

[11]  Fadi Al-Turjman,et al.  Deep learning, machine learning and internet of things in geophysical engineering applications: An overview , 2020, Microprocess. Microsystems.

[12]  A. Keyhani,et al.  An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran , 2010 .

[13]  John Twidell,et al.  The Weibull distribution function and wind power statistics , 1983 .

[14]  Chia-Hung Yeh,et al.  Machine Learning for Long Cycle Maintenance Prediction of Wind Turbine , 2019, Sensors.

[15]  N. Eskin,et al.  Wind energy potential of Gökçeada Island in Turkey , 2008 .

[16]  Ali Mostafaeipour,et al.  An analysis of wind energy potential and economic evaluation in Zahedan, Iran , 2014 .

[17]  Fadi Al-Turjman,et al.  Aiming for smart wind energy: A comparison analysis between wind speed forecasting techniques , 2019 .

[18]  Mojtaba Nedaei,et al.  Wind resource assessment in Hormozgan province in Iran , 2014 .

[19]  A. Ouammi,et al.  Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy) , 2010 .

[20]  C. O. Okoye,et al.  Technical and economic analysis of wind energy potential in Uzbekistan , 2019, Journal of Cleaner Production.

[21]  Gilles Notton,et al.  Technical and economic analysis of large-scale wind energy conversion systems in Algeria , 2013 .

[22]  T. Chang.  Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application , 2011 .

[23]  Ali Mostafaeipour,et al.  Using different methods for comprehensive study of wind turbine utilization in Zarrineh, Iran , 2013 .

[24]  A. Brett,et al.  The goodness of fit of the weibull and rayleigh distributions to the distributions of observed wind speeds in a topographically diverse area , 1985 .

[25]  Fadi Al-Turjman,et al.  Feasibility analysis of solar photovoltaic-wind hybrid energy system for household applications , 2020, Comput. Electr. Eng..

[26]  Ali Mostafaeipour,et al.  EVALUATION OF WIND ENERGY POTENTIAL AS A POWER GENERATION SOURCE FOR ELECTRICITY PRODUCTION IN BINALOOD, IRAN , 2013 .

[27]  A. Tizpar,et al.  Wind resource assessment and wind power potential of Mil-E Nader region in Sistan and Baluchestan Province, Iran – Part 1: Annual energy estimation , 2014 .

[28]  C. O. Okoye,et al.  Assessing the feasibility of wind energy as a power source in Turkmenistan; a major opportunity for Central Asia's energy market , 2019, Energy.

[29]  A. Sedaghat,et al.  Assessing the wind energy potential locations in province of Semnan in Iran , 2011 .

[30]  Farhan Ullah,et al.  A New Hybrid Approach of Clustering Based Probabilistic Decision Tree to Forecast Wind Power on Large Scales , 2021, Journal of Electrical Engineering & Technology.

[31]  R. Rahin Batcha,et al.  A Survey on IOT Based on Renewable Energy for Efficient Energy Conservation Using Machine Learning Approaches , 2020, 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE).

[32]  Piotr Wais,et al.  Two and three-parameter Weibull distribution in available wind power analysis , 2017 .

[33]  Tsang-Jung Chang,et al.  Assessment of wind characteristics and wind turbine characteristics in Taiwan , 2003 .

[34]  Hussein Al-Zoubi,et al.  IoT Applications in Wind Energy Conversion Systems , 2019 .

[35]  Ali Mostafaeipour,et al.  Economic evaluation of small wind turbine utilization in Kerman, Iran , 2013 .

[36]  Bekir Adem Çakmakçı,et al.  Evaluation of wind energy potential: a case study , 2020, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

[37]  Batın Demircan,et al.  IoT and Cloud Based Remote Monitoring of Wind Turbine , 2019, Celal Bayar Üniversitesi Fen Bilimleri Dergisi.

[38]  Omid Nematollahi,et al.  Assessment of wind energy in Iran: A review , 2012 .