Systems, methods, and devices are provided for monitoring precipitation. An example rain gauge device for use in such monitoring generally includes a first basin including at least one outlet for forming and releasing droplets of moisture, and at least two electrical contacts disposed proximate to the at least one outlet. A closed circuit is formed between the at least two electrical contacts when a droplet of moisture, released by the at least one outlet, contacts the at least two electrical contacts. The rain gauge device then also includes a processor communicatively coupled to the at least two electrical contacts. The processor is configured to determine presence of a moisture event based on the closed circuit formed by the droplet and the at least two electrical contacts and, in response to the determination, transmit an indication of the moisture event to a computing device.

Systems, methods, and devices are provided for monitoring precipitation. An example rain gauge device for use in such monitoring generally includes a first basin including at least one outlet for forming and releasing droplets of moisture, and at least two electrical contacts disposed proximate to the at least one outlet. A closed circuit is formed between the at least two electrical contacts when a droplet of moisture, released by the at least one outlet, contacts the at least two electrical contacts. The rain gauge device then also includes a processor communicatively coupled to the at least two electrical contacts. The processor is configured to determine presence of a moisture event based on the closed circuit formed by the droplet and the at least two electrical contacts and, in response to the determination, transmit an indication of the moisture event to a computing device.

Predicting energy production for energy generating assets, including: receiving current and forecasted meteorological data associated with a location of an energy generating asset; and predicting an energy production value produced by the energy generating asset at a predetermined time based on the current and forecasted meteorological data using a trained model for the energy generating asset, the trained model being trained using a machine learning algorithm that utilizes historical meteorological data associated with the location of the energy generating asset and historical production capability data associated with a historical production capability of the energy generating asset.

Predicting energy production for energy generating assets, including: receiving current and forecasted meteorological data associated with a location of an energy generating asset; and predicting an energy production value produced by the energy generating asset at a predetermined time based on the current and forecasted meteorological data using a trained model for the energy generating asset, the trained model being trained using a machine learning algorithm that utilizes historical meteorological data associated with the location of the energy generating asset and historical production capability data associated with a historical production capability of the energy generating asset.

Calculating energy loss during an outage, including: determining that windspeed data indicating device windspeeds measured at an energy generating device are unavailable within a particular time duration; receiving meteorological data associated with a site location of the energy generating device, the meteorological data including meteorological windspeed data collected within the particular time duration; and predicting one or more estimated device windspeeds at the energy generating device during the particular time duration based on the meteorological data using a trained model for the energy generating device, the trained model being trained using a machine learning algorithm that utilizes historical meteorological windspeed data associated with the site location collected during a previous time duration and corresponding historical device windspeed data measured at the energy generating device during the previous time duration.

Calculating energy loss during an outage, including: determining that windspeed data indicating device windspeeds measured at an energy generating device are unavailable within a particular time duration; receiving meteorological data associated with a site location of the energy generating device, the meteorological data including meteorological windspeed data collected within the particular time duration; and predicting one or more estimated device windspeeds at the energy generating device during the particular time duration based on the meteorological data using a trained model for the energy generating device, the trained model being trained using a machine learning algorithm that utilizes historical meteorological windspeed data associated with the site location collected during a previous time duration and corresponding historical device windspeed data measured at the energy generating device during the previous time duration.

The accuracy of photovoltaic simulation modeling is predicated upon the selection of a type of solar resource data appropriate to the form of simulation desired. Photovoltaic power simulation requires irradiance data. Photovoltaic energy simulation requires normalized irradiation data. Normalized irradiation is not always available, such as in photovoltaic plant installations where only point measurements of irradiance are sporadically collected or even entirely absent. Normalized irradiation can be estimated through several methodologies, including assuming that normalized irradiation simply equals irradiance, directly estimating normalized irradiation, applying linear interpolation to irradiance, applying linear interpolation to clearness index values, and empirically deriving irradiance weights. The normalized irradiation can then be used to forecast photovoltaic fleet energy production.

This application relates to an input data generating apparatus for generating forcing data used as input data for a climate change prediction model. In one aspect, the apparatus includes a memory storing instructions and a processor configured to, by executing the instructions, collect new ground type data from land-use harmonization (LUH) data that is restored through history database of the global environment (HYDE) and provided by the coupled model inter-comparison project (CMIP). The processor may also collect existing ground type data calculated by an existing model in a previous phase of the CMIP, generate aggregated ground type data by data-aggregating the new ground type data and the existing ground type data, with priority to the new ground type data. The processor may further generate the forcing data from the aggregated ground type data by performing a distortion correction on a data distortion that occurs during the data aggregation.

This application relates to an input data generating apparatus for generating forcing data used as input data for a climate change prediction model. In one aspect, the apparatus includes a memory storing instructions and a processor configured to, by executing the instructions, collect new ground type data from land-use harmonization (LUH) data that is restored through history database of the global environment (HYDE) and provided by the coupled model inter-comparison project (CMIP). The processor may also collect existing ground type data calculated by an existing model in a previous phase of the CMIP, generate aggregated ground type data by data-aggregating the new ground type data and the existing ground type data, with priority to the new ground type data. The processor may further generate the forcing data from the aggregated ground type data by performing a distortion correction on a data distortion that occurs during the data aggregation.

A photovoltaic system can include multiple photovoltaic power inverters that convert sunlight to power. An amount of power for each of the inverters can be measured over a period of time. These measurements, along with other data, can be collected. The collected measurements can be used to generate artificial neural networks that predict the output of each inverter based on input parameters. Using these neural networks, the total solar power generation forecast for the photovoltaic system can be predicted.