energy-solar

UC Berkeley Analytics Project

Name Forecast of Renewable Energy Consumption Insight into Solar - Exploration into Monthly Solar Energy using four distinct techniques: Autoregression, Linear Regression, Multi-layer Perceptron Regressor, and Random Forest Regressor

Motivation: The world of solar has advanced dramatically, solar’s clean energy is becoming more accessible every day. High residential electricity costs prompt homeowners to turn to solar for financial savings and environmental benefits. These trends motivated us to take a closer look at residential solar energy consumption in the United States.

Data Source:

• Solar Energy Consumption by the residential sector from U.S. Energy Information Administration (https://www.eia.gov/totalenergy/data/browser/)
• Average Retail Price of Residential Electricity from U.S. Energy Information Administration (https://www.eia.gov/totalenergy/data/browser/?tbl=T09.08#/?f=M)
• Residential Energy Demand Temperature Index (REDTI) from NOAA National Oceanic and Atmospheric Administration (https://www.ncdc.noaa.gov/societal-impacts/redti/USA)

Technology stack used:

• pandas, numpy, matplotlib, seaborn, sklearn, statsmodels, plotly
• Flask, Html-css, bootstrap, bootswatch, Javascript, Jsonify

References:

• Kaggle:
  • https://www.kaggle.com/nathanto/seds-1960-2014F
  • https://www.kaggle.com/sohier/30-years-of-european-solar-generation
• Census:
  • https://www.census.gov/library/publications/2011/compendia/statab/131ed/energy-utilities.html

Process:

• Exploration and Model training described in Energy Solar ppt final version.pdf

Project Contributors

Manuela Machado	Guirlyn Olivar