Solar Radiance Measurements In California

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Solar Irradiance Measurements in California, 2014-2016

High-Resolution GHI and DNI Measurements

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About this dataset

This dataset provides in-depth and quality-controlled solar irradiance measurements collected from 2014 to 2016 in California. With its comprehensive array of one-minute resolution Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) readings, as well as satellite imagery, Numerical Weather Prediction forecasts and weather data, this data is an invaluable resource for researchers or anyone looking to gain insight into the world of solar energy or forecasting. This dataset has the potential to allow benchmarking studies and development of applications related to solar energy production & utilization. Its range of resources allows for a multitude of avenues for exploration – drawing insights about the environment, climate change dynamics and much more! Make use of this quality dataset today!

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How to use the dataset

How to use this Dataset

This dataset contains a comprehensive, quality-controlled set of solar irradiance ground measurements from 2014 - 2016 in California, United States. With the datasets provided in one-minute resolution global horizontal irradiance (GHI) and direct normal irradiance (DNI), this dataset can be used Used for benchmarking, development and research purposes. In this guide we will walk through how to best utilize these datasets for your own project or research work.

First off, it is essential that you understand the different types of solar irradiance measurements you will find in this dataset:
-Global Horizontal Irradience (GHI): GHI is the total amount of radiation per unit area from direct and indirect sources coming from above the atmospheric horizon over a given time.
-Direct Normal Irradience (DNI): DNI refers to that amount of sunlight incident on a surface which is directly exposed towards source

Knowing these different types of radiation measured here at varying scales helps contextualize what kind data is being provided in the form of numerical values found within each column within the datasets themselves.

The columns include: timestamp, ghi (Global Horizontal Irradience), emissions, dni (direct normal irradiation) as well as dhi diffuse horizontal Irisiation). You can use these columns individually or together depnding on your goal(s). For example if you’re trying to compare year by year trends over a 3 year period you could separate out each individual measurement separately depending on their respective categories into data frames for further analysis(e.g plotting against each other etc). Additionally if utilizing all three together may be beneficial if attempting predictions regarding future values based off previous trends related to various converging factors(e.g., wind speed seasonality etc.). Allowing yourself to become familiar with features present int he csv file itself would go a long way when it comes manipulating files containing such intricate information .

In addition Sun Position Calculations such as declination angle , zenith angle are both calculated using date/time data provided with initial import available via sky_images satellite imagery ectYou'll also find Numerical Weather Prediction forecasts & weather conditions taken into account given certain temperatures ,wind speeds ,humidity levels all contributing concerning influential factors which could impact overall value either directly or indirectly . It should both challenging & exciting harnessing such specific climate conditions resulting due influences potentially caused by human activity ect

In conclusion Now that you know

Research Ideas

• Developing an Artificial Intelligence (AI) system to forecast solar irradiance levels using satellite imagery and weather forecasts.
• Using the GHI and DNI measurements to analyze the trend in total global radiation over a set period of time at various venues located in California over time, gaining insights into long-term solar energy production potential.
• Leveraging the high-resolution data to create spatio-temporal models that can be used by energy planners and engineers for optimizing the design of renewable energy systems or constructing profiles of individual customer usage with higher accuracy than traditional methods

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Folsom_irradiance.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .