TEMPERATURE AGENT

DESCRIPTION

The "Temperature Agent" project is a simple software tool designed to monitor and notify users when the temperature falls outside a specified user-defined range. It operates by continuously checking the current temperature and comparing it to the predefined minimum and maximum temperature values set by the user.

USE-CASE

The "Photovoltaic Power Predictor" is designed for forecasting solar power generation from photovoltaic (PV) systems. This prediction model utilizes a wide range of dynamic environmental parameters, including temperature, temperature range, wind speed, longitude, and latitude, among others, to provide highly accurate estimates of PV system output.

Table of Contents

About Installation Usage Contributing License

ABOUT

Overview

The "Photovoltaic Power Predictor" is an innovative software solution designed to accurately forecast and predict solar power generation from photovoltaic (PV) systems. This powerful tool leverages dynamic environmental parameters, including temperature, temperature range, wind speed, longitude, and latitude, to provide real-time and precise estimates of PV system output.

Main Features

Environmental Data Integration: The predictor collects and integrates a wide range of dynamic environmental data, such as real-time temperature, temperature range, wind speed, and geographical coordinates, to create a comprehensive and up-to-date picture of the conditions affecting solar power generation.

Accurate Solar Energy Forecasts: By processing this wealth of data, the tool generates highly accurate and reliable forecasts of photovoltaic power generation. These forecasts empower users to make informed decisions regarding energy consumption, storage, and distribution.

Energy Optimization: The "Photovoltaic Power Predictor" assists in optimizing the performance and efficiency of PV systems by considering various factors that impact solar power generation. This leads to better resource utilization and enhanced energy management.

Run Locally

git clone https://github.com/Nitesh-11/HackAI_Hack-230606

install dependencies

pip install -r requirements.txt

run the agents

python modelAgent.py
python temperatureAgent.py
python webAgent.py

start the frontend

streamlit run front2.py

LIBRARIES

1.numpy 2.pandas 3. matplotlib 4.sklearn 5.XGBoostRegressor- Regression Model 5.streamlit

API :-

1. https://open-meteo.com/
2. https://github.com/OpenBMB/AgentVerse

Training Model-:

1. https://www.kaggle.com/code/aryandeshpande/solar-energy-prediction
2. https://colab.research.google.com/drive/13TGW0gZs7j8uxnX4n4fvbJ2ZWiOwdIGB