Solar energy Prediction Using ML

Keywords: Solar energy, Prediction,Machine learning

Background

Solar energy is a renewable and environmentally friendly power source that plays a critical role in meeting global energy needs. To effectively plan, integrate into the grid, and optimize the utilization of solar resources, accurate prediction of solar energy generation is essential. This paper introduces a solar energy prediction system that utilizes machine learning (ML) techniques to forecast the energy output from solar panels. The proposed system takes advantage of historical solar energy generation data and incorporates various weather-related features, including irradiance levels, temperature, humidity, and cloud cover. These data are collected from meteorological stations and sensors embedded in solar panels. ML models are trained on this data to identify patterns and correlations between weather conditions and solar energy production. Multiple ML algorithms, such as support vector machines (SVM), random forests (RF), are employed to develop predictive models. The dataset is divided into training, validation, and testing sets to enable model training and evaluation

Aim & Objectives

Aim
The main of this project is to develop accurate models for predicting solar energy generation to optimize planning and utilization of solar power resources
Objective
• Data Cleaning and pre-processing
• Data visualization
• Outlier detection
• Model Building

Research Methodology

various regression techniques like Linear regression,SVR,KNNR,DT,RF

Software & Tools

Jupyter, Google Collab

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