You will get Predicting long term solar energy
Project details
Develop predictive models using Random Forest Regressor (RFR) and Artificial Neural Networks (ANN) to forecast long-term solar energy production
Use historical energy data and meteorological parameters
Create ensemble approach for improved accuracy
Focus on Pakistani solar installations across 3 major cities
Evaluate the robustess and accuracy of the models using metrics RMSE, MAE, R² score
Use historical energy data and meteorological parameters
Create ensemble approach for improved accuracy
Focus on Pakistani solar installations across 3 major cities
Evaluate the robustess and accuracy of the models using metrics RMSE, MAE, R² score
Machine Learning Tools
NumPy, Open Neural Network Exchange, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, XGBoostWhat's included $50
These options are included with the project scope.
$50
- Delivery Time 15 days
- Number of Revisions 0
- Number of Model Variations 4
- Number of Scenarios 0
- Number of Graphs/Charts 5
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
- Source Code
Optional add-ons
You can add these on the next page.
Fast 10 Days Delivery
+$20
Additional Revision
+$5
Additional Model Variation
(+ 1 Day)
+$5
Additional Scenario
(+ 1 Day)
+$5
Additional Graph/Chart
(+ 1 Day)
+$5About Abdul
Data Analyst
Rawalpindi, Pakistan - 10:05 am local time
Steps for completing your project
After purchasing the project, send requirements so Abdul can start the project.
Delivery time starts when Abdul receives requirements from you.
Abdul works on your project following the steps below.
Revisions may occur after the delivery date.
Data gathering
EDA