Wind energy assessment and mapping using terrain nonlinear autoregressive neural network (TNARX) and wind station data

Lawan, S. M.; Abidin, Wan Azlan Wan Zainal

doi:10.1080/23311916.2018.1452594

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…Where v1, v2 are wind speed at height h1 and h2 and α is a friction factor of the area =0.1432 is used for neutral and stable condition [30][31].…”

Section: Wind Speed At Upper Altitudementioning

confidence: 99%

“…In Sarawak, few studies were conducted from 2012 to date. The studies are; introduction of topography for wind energy evaluation considering the complex terrain in Sarawak [26][27], ANN and GIS mapping of wind energy [28], wind power potential in Kuching [29], potential of T-FNNN in monthly wind speed prediction and energy mapping [30], using prediction model and ground data considering terrain variations and using terrain nonlinear soft computing methods [31]. The listed study concentrates in either one location or using models to predict the wind speed.…”

Section: Introductionmentioning

confidence: 99%

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Development of an Improved Hybrid Back propagation ANN for Low Wind speed prediction and Wind Energy Evaluation

Lawan¹,

Abidin²,

Hotoro³

2020

ARFMTS

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Wind energy is clean, reliable, and affordable renewable energy which can be harnessed during the day and night. Before a wind turbine is installed, wind resource assessment (WRA) must be conducted in order to evaluate the wind power potential. The most important parameter in WRA is the wind speed values. The traditional ways of measuring wind speed could not be relied on, due to time constraint and cost. Because of these problems, a prediction model using deep learning is proposed in paper to solve the lingering problem. The objective of this paper is to develop a machine learning, prediction model using available data. Wind data were obtained from Malaysia Meteorological Department (MMD) for a period of ten years starting from 2008-2018. The wind energy evaluation was conducted at 10m-40m meters, respectively. In the areas with limited data or without data a prediction model was developed using different Artificial Neural Networks (ANNs) structures. The model was trained, tested, and validated using measured wind speed in the nearby location. The optimized model in terms of less structure with high prediction accuracy was selected for the final prediction has a correlation value of 0.952. A detailed wind resource assessment was conducted in the areas based on most fitted wind speed distribution model. It was found that Weibull and Rayleigh fitted the wind speed in the areas examined. At the end of the analysis, low wind speed turbine was selected for the wind farm sitting; the results show that wind energy can be harnessed for small Pico scale application such as rural electrification, and grain grinding. Because, in all the cases the wind power density falls within class 1 (PD≤100 W/m 2). The outcomes of this study would be useful for policy makers to implement 3-Es Model (Earth-Energy and Empowerment) in rural and remote areas of Sarawak.

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“…Where v1, v2 are wind speed at height h1 and h2 and α is a friction factor of the area =0.1432 is used for neutral and stable condition [30][31].…”

Section: Wind Speed At Upper Altitudementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of an Improved Hybrid Back propagation ANN for Low Wind speed prediction and Wind Energy Evaluation

Lawan¹,

Abidin²,

Hotoro³

2020

ARFMTS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Wind energy resources can be clearly and efficiently estimated [10,11]. Wind speed is a disturbing hallmark in the estimation of wind energy [12,13]. Generally, depending on the observed wind speed data and probabilistic distribution functions, the frequency distribution function can be obtained [14].…”

Section: Introductionmentioning

confidence: 99%

Techno-economic assessment of wind energy conversion systems for power generation for the city of N'Djamena in Chad

et al. 2020

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Around the world, with a significant increase in installed capacity each year, wind power is one of the most profitable forms of renewable energy. In this study, three commercial wind turbines, namely Bonus 300kW/33, Bonus 1MW/54 and Vestas 2MW/80, were selected as large-scale wind power conversion systems (WECS) for the technical evaluation of production electricity for the N'Djamena site in Chad. The economic evaluations of these three WECS for power generation are calculated using the Cost Calculation Technique (PVC). Thus, the results obtained show that the highest capacity factor is 15.29% with Bonus 1MW/54 while the lowest at 11.39% with Bonus 300kW/33. The minimum average cost per kWh is 23 US$ / kWh with the 1 MW/54 Bonus, while the highest cost is 33.8 US$ / kWh with a Bonus of 300 kW / 33.

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“…The State is located between 109°36'E and 115040'E of longitude and 0°N and 5 0 N latitudes at a height between 400 to 1000 m above mean sea level. It has a total land area of approximately 124,000 square kilometers, which is almost equivalent to the total area of the other states forming the Malaysia Peninsular [3].…”

mentioning

confidence: 99%

https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/6393/3643

Lawan¹,

Abidin²,

Alhaji³

et al. 2020

IJIE

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It is generally known that the current system of energy generation based on limited resource fossil fuels (Coal, Gas and Petroleum) is not sustainable. Because the resources are finite, they are discovered and utilized. That is why most of the countries around the world have realized the current scenario of energy using dirty source is generating environmental worries such as greenhouse gases (GHG, acid rain etc. furthermore, increase in population and urbanization has raised the energy demand and consumption, but unfortunately, the fossil fuels are not evenly distributed around the world. The listed problems has necessitated searching of an alternative sources, that will be used today and tomorrow without compromising the future needs of energy. Among the clean sources (solar, hydro, fuel cell, wave tidal, geothermal, thermo electric generator), wind energy has emerged as the oldest technology applied by mankind to solve remote and immediate problems. In recent times, wind energy is become popular for electrical power generation in both grid-tie and non-grid mode. Wind energy is an indirect solar energy, the merits of using this resources is the wind availability in day and night. The potential of its development is huge, since the world's capacity is far larger than the world's total energy consumption [1]. Recent publication of wind energy association has shown that the wind energy is currently at boom stage, as more countries of the world developing medium and large wind energy farms. As a new global high-tech sector, more countries are taking advantage of this untapped source of renewable energy. Malaysia is trying to develop a reliable energy mix that will boost it economic, and urbanization

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Wind energy assessment and mapping using terrain nonlinear autoregressive neural network (TNARX) and wind station data

Cited by 6 publications

References 30 publications

Development of an Improved Hybrid Back propagation ANN for Low Wind speed prediction and Wind Energy Evaluation

Development of an Improved Hybrid Back propagation ANN for Low Wind speed prediction and Wind Energy Evaluation

Techno-economic assessment of wind energy conversion systems for power generation for the city of N'Djamena in Chad

https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/6393/3643

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