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Renewable Energy Forecasting API

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Detail Description

Abstract

Renewable Energy Forecasting focuses on predicting energy generation from sources like solar and wind using historical weather data, time series analysis, and machine learning models. Accurate forecasting helps optimize power grid management, reduce energy waste, and support sustainable energy planning.


Existing System

  1. Relies on manual estimation and basic statistical methods
  2. Limited accuracy due to weather variability
  3. Poor real-time adaptability
  4. Inefficient energy distribution and storage planning


Proposed System

  1. Uses machine learning and time series models (ARIMA, LSTM, Regression)
  2. Integrates real-time weather and historical data
  3. Provides accurate short-term and long-term forecasts
  4. Enhances grid stability and renewable energy utilization


Software Requirements

  1. Python
  2. Libraries: Pandas, NumPy, Scikit-learn, TensorFlow/Keras, Matplotlib
  3. Jupyter Notebook / VS Code
  4. Flask or FastAPI (for API deployment)


Hardware Requirements

  1. Minimum 4 GB RAM (8 GB recommended)
  2. Multi-core processor
  3. Internet connection for weather API data
  4. Optional GPU for deep learning models


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