del2infinity's goal is to shape a smart energy future through data-driven analytics. del2infinity leverages on Artificial Intelligence and proprietary algorithms based on statistical machine learning and pattern recognition to satisfy renewable power stakeholders, electricity utilities and smart grid players.

1. del2infinity Advantage Trends
In their pursuit for maximum revenues and minimum losses from
inefficient commitment in formal electricity markets, wind power
producers are tapping into advanced wind forecast technologies. One
general trend in wind forecast is to use physical models, including
Numerical Weather Prediction (NWP) models, meteorological data
and fluid flow models of the atmosphere to predict wind speeds
(Landberg et al., 2003). Wind speed forecasts are then transformed to
power forecasts using a transfer function to model wind plant output.
Statistical models, on the other hand, rely on historical and real-time
wind data to predict future values. Such statistical models generally
employ time series (for example, Markov chains) or neural network
analysis methods (Giebel et al., 2003).
Problem
Traditional wind power forecast approaches have two major
drawbacks: 1) complexity and expense due to site-specific additional
hardware installation, and 2) multiple points of uncertainty and error
amplification while downscaling from global meso-scale models and
transformation of wind speed to wind power. Since the transformation
of wind speed to wind power is nonlinear (Das, 2014), the error will
differ depending on whether wind is at low or high speeds. At low
wind speeds, forecasts tend to predict less wind power than is actually
generated, while at high wind speeds, the forecasts tend to predict
more power than is actually generated (Mauch et al., 2013).
Wind Power Forecast
del2infinity ensures proactive operation of wind assets through accurate
power forecast enabled by artificial intelligence thereby changing the
paradigm of traditional resource intensive forecast approaches.
Proactive Customers Advanced Technology Accurate Solution
del2infinity leverages on
Artificial Intelligence (AI)
and uses proprietary
algorithms based on
statistical machine learning
and pattern recognition.

2. Wind Forecast Engine
del2infinity Solution
Solution
del2infinity leverages on Artificial Intelligence (AI) and uses
proprietary algorithms based on statistical machine learning and
pattern recognition. del2infinity’s power-to-power AI forecast
methodology is complemented with parallel wind speed feedback
loops. del2infinity offers its services in the form of a dynamically
evolving cloud based forecast platform and wind power scheduling to
realize maximum revenues for wind power producers. del2infinity
uses the most advanced AI methods which ensures high accuracy
level, customizability, easy scalability and cost-effectiveness like
never before.
Additionally, to ensure that renewable plant owners are prepared for
extreme situations involving fast ramp up/down of power within a
short time-frame (Das and Mazumdar, 2013; Mazumdar et al., 2014),
del2infinity offers a renewable power variability analytics platform.
Benefits
The del2infinity Wind Forecast Engine is designed keeping in mind
country-specific electricity market mechanisms (CERC notifications
in case of India). Apart from being able to provide day-ahead forecast,
it can help customers take advantage of the provisions for
commitment revisions by providing short-term (3-8 hours) wind
power forecast. The del2infinity Wind Forecast Engine helps
customers minimize financial uncertainty by exploring cost savings
resulting from AI based wind power forecast compared to
conventional methods.
References:
Das, A.K., 2014. An empirical model of power curve of a wind turbine. Energy Syst. doi:10.1007/s12667-013-0101-5
Das, A.K., Mazumdar, B.M., 2013. A Statistical Model for Wind Power on the Basis of Ramp Analysis. Int. J. Green Energy 131010083126007.
Giebel, G., Brownsword, R., Kariniotakis, G., 2003. State of the art on short-term wind power prediction (ANEMOS deliverable report, D1.1).
Landberg, L., Giebel, G., Nielsen, H.A., Nielsen, T., Madsen, H., 2003. Short-term Prediction: An Overview. Wind Energy 6, 273–280. doi:10.1002/we.96
Mauch, B., Apt, J., Carvalho, P.M.S., Small, M.J., 2013. An effective method for modeling wind power forecast uncertainty. Energy Syst. 4, 393–417.
Mazumdar, B.M., Saquib, M., Das, A.K., 2014. An empirical model for ramp analysis of utility-scale solar PV power. Sol. Energy 107, 44–49.
We’d love to hear from you!
Email: contact@del2infinity.com
Phone: +91 962 058 7138
www.del2infinity.com
 Scalable
 Customizable
 Simple Interface
 Cost-effective
del2infinity’s thorough
understanding of Availability
Based Tariff and Unscheduled
Interchange mechanisms has
been at the heart of product
development