Wind Turbine Energy Prediction
In the past three decades, research and development in green energy has exploded, yielding hundreds of promising new technologies that can reduce our dependence on coal, oil, and natural gas. In this context, wind energy is a growing industry with high potential and relatively low production costs, supplying electricity to national grids worldwide. In 2015, wind energy supplied about 3.7% of global electricity. However, wind energy cannot be generated on demand, in the manner of traditional electricity generation due to a strong dependency on atmospheric phenomena.
Wind power gives variable power which is very consistent from year to year but which has significant variation over shorter time scales. In practice, the variations in thousands of wind turbines, spread out over several different sites and wind regimes, are smoothed. As the distance between sites increases, the correlation between wind speeds measured at those sites, decreases. Thus, while the output from a single turbine can vary greatly and rapidly as local wind speeds vary, as more turbines are connected over larger and larger areas the average power output becomes less variable and more predictable.
Management of wind energy uses forecasting methods, but predictability of any particular wind farm is low for short-term operation. For example, for any particular generator there is an 80% chance that wind output will change less than 10% in an hour and a 40% chance that it will change 10% or more in 5 hours. Therefore, efficient management of wind energy requires new and novel forecasting methods.
In this research, we propose the use of NeuCube as a novel methodology for modelling and understanding spatial and temporal data from multi-sensory networks. For wind energy forecasting, NeuCube can incorporate spatial information such as the geographical coordinates of wind turbines and temporal data from diverse variables such as wind velocity and wind direction. These unique NeuCube’s features allows not only to achieve better prediction accuracy but also to analyse the variation and correlation between the variables involved in wind energy production.
FIGURE. NeuCube 3D spiking neural network map (left) of a 13-wind turbine farm (dark green circles). Spatio-temporal relationships (lines) and activity (light green circles) of wind speed can be analysed for wind power prediction (right).
Related Papers and Benchmarking
The proposed methods and systems, when compared with traditional statistical and optimisation methods, showed superior results in the following aspects:
- Though the research is in an early state, we have exploited the online multisensory data modelling feature of the NeuCube for mapping the geographical position of wind turbines. We have exploited the online multisensory data-modelling feature of the NeuCube for modelling the wind speed and the wind direction, both simultaneously recorded from each turbine. This main feature allows researchers to discover new information and knowledge on how these two variables interact for forecasting the wind power production in different time windows (e.g. 30 mins, 1 hr, 6 hr. or 24 hr. ahead).
See also some of the related papers:
Kasabov, N., Scott, N. M., Tu, E., Marks, S., Sengupta, N., Capecci, E., Othman, M., Gholoami Doborjeh, M., Murli, N., Hartono, R., Espinosa-Ramos, J. I., Zhou, L., Alvi, F., Wang, G., Taylor, D., Feigin, V., Gulyaev, S., Mahmoud, M., Hou, Z. G., Yang, J. (2016). Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications. Neural Networks, 78, 1-14.
For this project, an R&D system is being developed. The system can be obtained subject to licensing agreement.
The developer of this project is: