https://github.com/hariprasath-v/machinehack_forecasting_solar_energy_efficiency

create a model to predict solar energy efficiency based on the measurements of various meteorological parameters over a period of time.
https://github.com/hariprasath-v/machinehack_forecasting_solar_energy_efficiency

exploratory-data-analysis machine-learning metpy pandas pvlib python solar-energy

Last synced: 21 days ago
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create a model to predict solar energy efficiency based on the measurements of various meteorological parameters over a period of time.

• Host: GitHub
• URL: https://github.com/hariprasath-v/machinehack_forecasting_solar_energy_efficiency
• Owner: hariprasath-v
• License: apache-2.0
• Created: 2023-05-10T07:18:56.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-05-10T07:52:10.000Z (about 3 years ago)
• Last Synced: 2025-03-02T13:50:36.848Z (over 1 year ago)
• Topics: exploratory-data-analysis, machine-learning, metpy, pandas, pvlib, python, solar-energy
• Language: HTML
• Homepage:
• Size: 10.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Machinehack_forecasting_solar_energy_efficiency

## Private Leaderboard

* Rank :28

* Score :2620.31278

### Competition hosted on Machinehack

### Problem

create a model to predict solar energy efficiency based on the measurements of various meteorological parameters over a period of time.

### Evaluation

#### Evaluation metric for this competition is Mean Square Error.

### Dataset

You can download the dataset here    

### Solution:

### Exploratory Data Analysis

#### The basic exploratory data analysis of the data,

* Numerical Distribution Analysis

* Correlation Analysis

* Stationary Analysis

#### The above analysis had done by using,

* pandas 

* numpy

* seaborn

* matplotlib

* statemodels

### Model

#### Trained each target label separately by using pycaret tool and the model was evaluated with an MSE score.

#### Final model was blended from the top 3 performing models.

### File information

machinehack-forecasting-solarenergy-efficiency-eda.ipynb[![Open in Kaggle](https://img.shields.io/static/v1?label=&message=Open%20in%20Kaggle&labelColor=grey&color=blue&logo=kaggle)](https://www.kaggle.com/code/hari141v/machinehack-forecasting-solarenergy-efficiency-eda)

 

mh-forecasting-solarenergy-efficiency-model.ipynb[![Open in Kaggle](https://img.shields.io/static/v1?label=&message=Open%20in%20Kaggle&labelColor=grey&color=blue&logo=kaggle)](https://www.kaggle.com/code/hari141v/mh-forecasting-solarenergy-efficiency-model)