Machine Learning for Predicting the Energy Production of a Solar Farm скачать в хорошем качестве

Machine Learning for Predicting the Energy Production of a Solar Farm

This example models the energy production of a photovoltaic plant as a function of weather conditions using a neural network. For this purpose, we start with a year of measurements every three hours of the following variables: -Distance to solar noon, in radians. -Daily average temperature, in degrees Celsius. -Daily average wind direction, in degrees (0-360). -Daily average wind speed, in meters per second. -Sky-cover on a five-step scale, from 0 (totally clear) to 4 (wholly covered). -Visibility, in kilometers. -Humidity, in percentage. -Average wind speed (period), average wind speed during the 3 hours de measure was taken in, in meters per second. -Average pressure (period), average barometric pressure during the 3 hours de measure was taken in, in mercury inches. -Power generated in Jules that 3 hours. We use the data science and machine learning platform Neural Designer to build the energy production model. Neural Designer allows you to develop artificial intelligence applications without programming. Some solutions for Neural Designer in the energy sector are the following: -System modeling. -Process modeling. -Performance optimization. -Energy generation prediction. -Energy demand prediction. -Predictive maintenance. Download the data set used in this example from https://www.neuraldesigner.com/learni... Check the Neural Designer website at https://www.neuraldesigner.com/. You can try Neural Designer for free now at: https://www.neuraldesigner.com/free-t....