+919888176888
+919888176888
english english
Project Mentor Project Mentor
Cancel
Expand Menu
0
Sign in
Sign up
0 My cart ₹0.00
Shopping cart
Close
Cart

Your cart is empty, and it looks like you haven’t added anything yet.

Click to view
-20%

Solar Power Generation Prediction

0 Orders 0 Wish listed

₹4,999.00

Qty
Total price:
  ₹4,999.00

Detail Description

1. Abstract

Solar Power Generation Prediction is a machine learning and time-series forecasting project designed to predict the amount of electricity generated by solar photovoltaic (PV) systems. The system analyzes historical solar power generation along with factors such as solar irradiance, temperature, humidity, cloud cover, wind speed, and time-based seasonal patterns. Machine learning and deep learning models such as Random Forest, XGBoost, ARIMA, LSTM, and GRU can be used to forecast future solar power generation. The system can support renewable energy planning, grid management, energy storage, and efficient utilization of solar resources.

2. Objectives

  1. Predict future solar power generation.
  2. Analyze historical solar energy production patterns.
  3. Study the impact of weather and environmental conditions.
  4. Identify daily and seasonal generation trends.
  5. Apply machine learning and time-series forecasting techniques.
  6. Support renewable energy planning and management.
  7. Improve solar power generation estimation.
  8. Assist grid and energy-storage planning.
  9. Provide an interactive visualization and forecasting dashboard.

3. Existing System

Traditional solar power estimation may depend on:

  1. Historical generation records.
  2. Manual calculations.
  3. Weather forecasts.
  4. Solar irradiance measurements.
  5. Fixed generation assumptions.
  6. Expert analysis.

Limitations:

  1. Weather conditions can change rapidly.
  2. Manual forecasting is time-consuming.
  3. Sudden cloud cover can affect generation.
  4. Complex relationships between weather and generation may be missed.
  5. Large historical datasets are difficult to analyze manually.

4. Proposed System

The proposed system uses machine learning and time-series models to predict future solar power generation.

Workflow:

Historical Solar Data + Weather Data → Preprocessing → Feature Engineering → ML/DL Model → Power Generation Forecast → Visualization

Possible input features:

  1. Solar irradiance
  2. Temperature
  3. Humidity
  4. Cloud cover
  5. Wind speed
  6. Historical power generation
  7. Hour
  8. Day
  9. Month
  10. Season
  11. Solar position/time-related features

Possible algorithms:

  1. Linear Regression
  2. Random Forest
  3. XGBoost
  4. ARIMA/SARIMA
  5. LSTM
  6. GRU
  7. CNN-LSTM

5. Implementation Procedure

  1. Dataset Collection – Collect historical solar generation and weather data.
  2. Data Preprocessing – Handle missing values, duplicates, outliers, and incorrect timestamps.
  3. Exploratory Data Analysis – Analyze generation patterns and relationships with weather variables.
  4. Feature Engineering – Create lag values, rolling averages, time-based features, and weather-related features.
  5. Data Splitting – Use chronological train, validation, and test sets to avoid time-series leakage.
  6. Model Development – Train suitable ML or deep learning forecasting models.
  7. Model Evaluation – Use MAE, MSE, RMSE, MAPE, and R² where appropriate.
  8. Power Forecasting – Generate predictions for future time periods.
  9. Visualization – Display actual versus predicted generation, trends, and forecast charts.
  10. Application Development – Build an interactive dashboard using Streamlit or Flask.

6. Software Requirements

  1. Python
  2. Pandas
  3. NumPy
  4. Scikit-learn
  5. TensorFlow/Keras or PyTorch
  6. Statsmodels
  7. Matplotlib/Seaborn/Plotly
  8. Streamlit or Flask
  9. Jupyter Notebook / VS Code / Google Colab
  10. Solar power and weather dataset

7. Hardware Requirements

  1. Intel Core i5 or equivalent
  2. Minimum 8 GB RAM
  3. 20 GB+ free storage
  4. NVIDIA GPU recommended for LSTM/GRU training
  5. Windows/Linux
  6. Standard keyboard and mouse
  7. Internet connection

8. Advantages

  1. Predicts future solar power generation.
  2. Helps analyze renewable energy trends.
  3. Supports solar farm planning.
  4. Assists grid and energy-storage management.
  5. Can incorporate real-time or historical weather information.
  6. Reduces manual forecasting effort.
  7. Supports efficient renewable energy utilization.
  8. Helps identify periods of high and low generation.
  9. Provides visual and data-driven forecasting insights.


No review given yet!

Fast Delivery all across the country
Safe Payment
7 Days Return Policy
100% Authentic Products

You may also like

View all

Blockchain-Based Model Ownership Verification

₹4,999.00

Decentralized Federated Learning

₹4,999.00

Cryptocurrency Fraud Detection

₹4,999.00

Smart Contract Vulnerability Detection using ML

₹4,999.00

Blockchain-Based E-Voting

₹4,999.00

Similar products

Plant Disease Detection

₹4,999.00

Leaf Species Classification

₹4,999.00

Fruit Quality Classification

₹4,999.00

Weed Detection using Deep Learning

₹4,999.00

Crop Yield Prediction using LSTM

₹4,999.00

Drought Prediction using LSTM

₹4,999.00

Forest-Fire Prediction

₹4,999.00

Satellite-Based Land-Cover Classification

₹4,999.00

Flood Prediction using Spatiotemporal Networks

₹4,999.00

Air-Quality Forecasting using LSTM

₹4,999.00

Out of stock

Brain Tumour Detection from MRI

₹4,999.00

Pneumonia Detection from Chest X-rays

₹4,999.00

Solar Power Generation Prediction
₹4,999.00 ₹0.00
₹4,999.00
4999