2. Forecasting solar energy efficiency: Solar energy is one of the most environmentally friendly sources of energy. It is possible to forecast the output power of the solar panels during different time of the day, depending on different meteorological parameters over a period. For example, DHI (Diffused Horizontal Irradiance) represents the solar radiation that does not arrive on a direct path from the sun but has been scattered by clouds and particles in the atmosphere and comes equally from all directions. Knowing the past values of thousands of the data features such as time stamp (date and time of measurement), temperature, dew point, surface albedo, pressure, wind direction, wind speed, Ozone (Dobson Units), cloud type, solar zenith angle, precipitable water, relative humidity and other potential relevant parameters during those past times and having measured DHI value in Watts/m2 as of three levels (0: No Power, 1: 1 Watts/m2 to 300 Watts/m2 and 2: above 300 Watts/m2) during those past time slots, for a future time slot forecast the level of DHI, provided that all those data features are available (and also show the probabilities).
3. Predicting wind turbines’ output power levels under different conditions: Wind turbines as a source of renewable energy play a crucial rule in battle against global warming and having a greener and cleaner environment. Butterfly AI can help designers and wind turbine technology firms accurately predict the output power of the wind turbines under different conditions to optimise the process. For example, you may divide the output power range of wind turbine (if it is between 0KW and 2000KW) into ten buckets/classes of (Class 1 between 0 and 200KW, …, Class 10 between 1800KW and 2000KW) and then predict under certain conditions into which bucket, a future unseen prediction sample will fall in. Having measured the output power and established the data features of the selected wind turbines during thousands of past time snapshots and under different conditions including wind turbine ID, absolute wind direction, outdoor temperature, min outdoor temperature, max outdoor temperature, std of outdoor temperature, grid frequency, min grid frequency, max grid frequency, std of grid frequency, grid voltage, grid voltage min, grid voltage max, grid voltage std, rotor speed, rotor speed min, rotor speed max, rotor speed std, rotor bearing temperature, min of rotor bearing temperature, max of rotor bearing temperature, std of rotor bearing temperature, absolute wind direction, nacelle angle, pitch angle, min pitch angle, max pitch angle, std of the pitch angle, hub temperature, min hub temperature, max hub temperature, std of hub temperature, generator converter speed, min generator converter speed, max generator converter speed, std of hub temperature, generator speed, min generator speed, max generator speed, std of generator speed, generator bearings No. 1 to N temperature, min generator bearings No. 1 to N temperature, max generator bearings No. 1 to N temperature, std of generator bearings No. 1 to N temperature, generator stator temperature, min generator stator temperature, max generator stator temperature, std of generator stator temperature, gearbox bearings No. 1 to N temperature, min gearbox bearings No. 1 to N temperature, max gearbox bearings No. 1 to N temperature, std of gearbox bearings No. 1 to N temperature, gearbox inlet temperature, min gearbox inlet temperature, max gearbox inlet temperature, std of gearbox inlet temperature, gearbox oil sump temperature, min gearbox oil sump temperature, max gearbox oil sump temperature, std of gearbox oil sump temperature, nacelle angle, min nacelle angle, max nacelle angle, std of nacelle angle, nacelle temperature, min nacelle temperature, max nacelle temperature, std of nacelle temperature, and having established to which power bucket/class each one of those past measurement slots (i.e. training samples) belong and labelled them as Classes 1 to 10, predict and establish what level of power a wind turbine will generate under a future scenario (i.e. an unseen prediction sample). If you wish to predict the output power as continuous numbers rather than categories or classes, you may predict the winning bucket as winning class of the prediction process and then divide that bucket into further M more granular sub-buckets and perform further rounds of training and prediction across the range of the winning bucket to get the winning sub-bucket and take the average value of that sub-bucket as the final continuous output value of the prediction. Please see the overview page for more details regarding the prediction with continuous numbers.
4. Diagnose/Classify/Predict existing anomaly (or a developing anomaly (unusual events)) across mobile cellular wireless voice and data networks: Automated diagnosis, detection, or prediction of anomalies will help mobile operators to take pre-emptive actions to prevent disruption across the mobile cellular networks or reduce the cost and time required to fix the anomalies and network faults. Having performed thousands of measurements and recorded past network event logs and having the data features such as Physical Resource Block (PRB) usage across Uplink (UL), PRB usage across Downlink (DL), mean data throughput across DL, max data throughput across DL, mean data throughput across UL, max data throughput across UL, mean User Equipment (UE) utilisation across DL, max UE utilisation across DL, max UE utilisation across UL, mean UE utilisation across UL, total power consumption across UL, total power consumption across DL, and any other relevant data features, and having correctly verified and labelled the detected anomalies within those past events (0: no anomaly, 1: anomaly, the label and target of classification), in a live network detect an existing anomaly or predict a developing anomaly (and also present the certainty (probability)).
7. NASA star classification: Knowing the data feature of past known stars including L -- L/Lo, R -- R/Ro, AM -- Mv, Color -- General Color of Spectrum, Spectral Class -- O,B,A,F,G,K,M / SMASS and knowing each one of those pat starts belongs to one of the following classes, Red Dwarf, Brown Dwarf, White Dwarf, Main Sequence, Super Giants, Hyper Giants, classify and decide whether any newly observed star belongs to one of the classes, Red Dwarf, Brown Dwarf, White Dwarf, Main Sequence , Super Giants, Hyper Giants.
8. NASA hazardous asteroid prediction: Knowing the data feature of past known asteroids including Absolute Magnitude, Est Dia in KM(min), Est Dia in KM(max), Est Dia in M(min), Est Dia in M(max), Est Dia in Miles(min), Est Dia in Miles(max), Est Dia in Feet(min), Est Dia in Feet(max), Close Approach Date, Epoch Date Close Approach, Relative Velocity km per sec, Relative Velocity km per hr, Miles per hour, Miss Dist.(Astronomical), Miss Dist.(lunar), Miss Dist.(kilometers), Miss Dist.(miles), Orbiting Body, Orbit ID, Orbit Determination Date, Orbit uncertainity, Minimum Orbit Intersection, Jupiter Tisserand Invariant, Epoch Osculation, Eccentricity, Semi Major Axis, Inclination, Asc Node Longitude, Orbital Period, Perihelion Distance, Perihelion Arg, Aphelion Dist, Perihelion Time, Mean Anomaly, Mean Motion, Equinox and knowing whether each one of those past asteroids was actually hazardous or not, decide whether any new approaching asteroid to earth is going to be hazardous or not.