<jupyter_start><jupyter_text>Solar Production Data Analysis ObjectiveThis notebook will analysis solar production data using the data science lifecyce outlined here: linkhttps://docs.google.com/document/d/1gKr6j7s02hjilNhqYrjDK7t6jqR092hzezn-MB09EZo/editheading=h.mbjsiz6n6jlo Contents:1. Business Understanding2. Data Understanding3. Data Preparation4. Modeling5. Evaluation6. Deploymentpython version: 3.9.0requirements:see imports  1 Business Understanding Sections 1.1 Question DefinitionFirst, define your question(s) of interest from a business perspective.  1.2 Success CriteriaWhat is the goal of the project you propose, and what are the criteria for a successful or useful outcome? Assess the situation, including resources, constraints, assumptions, requirements.  1.3 Statistical Criteria Then, translate business objective(s) and metric(s) for success to data mining goals. If the business goal is to increase sales, the data mining goal might be to predict variance in sales based on advertising money spent, production costs, etc.  1.4 Project PlanFinally, produce a project plan specifying the steps to be taken throughout the rest of the project, including initial assessment of tools and techniques, durations, dependencies, etc.What is the goal of this project?What are the criteria for a successful outcome?Business goals are:What will the production be of a new system?What will the performance be of a new system?Other questions to answer if time permits:What is the quality of the data?understand the fleet better - how many systems, when were they installedindentify and predict poor performance?how might we improve performance?Can we predict future production?  1.1 Question DefinitionFirst, define your question(s) of interest from a business perspective.<jupyter_code>"""
Solar systems convert light energy from the sun to electricity. The amount of electricity produced is called "production" and the amount of electricity produced in reality compared to expectation is called "performance".
A commercial and residential solar installer is interested to learn if they can predict the production and performance of a new solar system.
They have provided data on the production and performance of their fleet with the hopes that it can be used as training data for a model.
Among other things, the model and analysis should tell them basic information, like growth of their fleet, average performance, and top performing systems, but also advanced statistics, like if the financial company is correlated to performance, which months are the best to install a new system, and what is the best estimation for annual degradation.
"""<jupyter_output><empty_output><jupyter_text>1.2 Success CriteriaWhat is the goal of the project you propose, and what are the criteria for a successful or useful outcome? Assess the situation, including resources, constraints, assumptions, requirements.   1.2.1 Goal 1.2.2 Criteria for success 1.2.3 Resources 1.2.4 Constraints 1.2.5 Assumptions 1.2.6 Requirements  1.3 Statistical Criteria Then, translate business objective(s) and metric(s) for success to data mining goals. If the business goal is to increase sales, the data mining goal might be to predict variance in sales based on advertising money spent, production costs, etc.  Load Data<jupyter_code>df = pd.import_data()<jupyter_output><empty_output>
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