Wind speed forecasting in Nepal using self-organizing map-based online sequential extreme learning machine
Edited book (chapter)
Chapter Title | Wind speed forecasting in Nepal using self-organizing map-based online sequential extreme learning machine |
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Book Chapter Category | Edited book (chapter) |
ERA Publisher ID | 1821 |
Book Title | Predictive Modelling for Energy Management and Power Systems Engineering |
Authors | Sharma, Neelesh (Author) and Deo, Ravinesh (Author) |
Editors | Deo, Ravinesh, Samui, Pijush and Roy, Sanjiban Sekhar |
Page Range | 437-484 |
Chapter Number | 14 |
Number of Pages | 48 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISBN | 9780128177723 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/B978-0-12-817772-3.00014-8 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/B9780128177723000148 |
Abstract | Renewable energy is often depicted as a clean source of energy and could have an effect in minimizing environmental impacts by reducing global warming and mitigating the greenhouse effect (Panwar et al., 2011; Wang and Han, 2014). A transition from fossil fuels to renewable sources has been growing in the past 30 years since the price of oil seems to increase constantly and, in contrast, there is a decline in the cost of renewable sources of energy (Herzog et al., 2018). Renewable energy was first adopted by most countries as an integral aspect of national energy policy goals after the 1973 oil crisis (Nepal, 2012). It also ignited interest in wind energy, water pumps, power supply in remote areas, and production of grid electricity powered by wind (Herzog et al., 2018). Among other renewable sources, wind energy has become a major attraction in today’s world because of its low pollution emissions and high efficiency (Wu and Hong, 2007). Wind energy is accepted globally as a clean energy ource and the cheapest replacement to coal (Nepal, 2012; Herzog et al., 2018). A 24% average annual growth in wind energy production across the world has been observed since 1990. Based on the trending decline in cost, analysts had forecasted that by the end of 2015 the electricity production cost of wind reached 2.5 US cents/KWh; lower than most fossils fuels (Herzog et al., 2018). According to a Greenpeace organization plan, by 2020 12% of all electricity production should be achieved from wind energy (Wang et al., 2011). Nepal, the focus of this chapter, is one of the developing countries that has been in the dark ages of the electricity crisis, with persistent power cuts since 2006 (Laudari, 2016). At present, hydroelectricity is the main source of grid electricity in Nepal, and only 56% of households in Nepal have access to the grid. Fig. 14.1 shows the power supply and demand for electricity from the year 2009 to 2018. It is observed that both supply and demand is increasing constantly throughout the year, however, the supply of power has not met its demand. According to Solar and Wind Energy Resource Assessment (SWERA), Nepal has the potential to generate 2100 MW electricity from solar, 50 MW from microhydro, and 3000 MW from wind (SWERA, 2006; Gurung et al., 2013). Thus wind energy seems to be a viable solution as an alternative source to overcome the yearly drought of electricity in Nepal. |
Keywords | renewable energy; wind energy; Nepal |
ANZSRC Field of Research 2020 | 419999. Other environmental sciences not elsewhere classified |
469999. Other information and computing sciences not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Sciences |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5yq6/wind-speed-forecasting-in-nepal-using-self-organizing-map-based-online-sequential-extreme-learning-machine
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