An interactive web application developed with Streamlit, designed for making predictions using various machine learning models. The app dynamically generates forms and pages from JSON configuration files. ⭐ If you found this helpful, consider starring the repo!
🔍 Problem Description:
I am working on a project to predict energy consumption for a building or city based on historical energy consumption data, weather conditions (temperature, humidity, etc.), occupancy, and usage patterns. The goal is to improve energy management and optimization by accurately forecasting future energy consumption.
🧠 Model Description:
I plan to use time series forecasting models such as LSTM (Long Short-Term Memory) or ARIMA (AutoRegressive Integrated Moving Average). These models are well-suited for capturing temporal patterns in energy consumption data. LSTM can handle complex dependencies over time, while ARIMA is useful for trend and seasonality detection in energy usage.
⏲️ Estimated Time for Completion:
5 days to gather data, implement the model, and perform testing and validation.
🎯 Expected Outcome:
The model is expected to provide accurate predictions of future energy consumption, helping to optimize energy usage, reduce costs, and improve sustainability efforts. The outcome will also support more effective decision-making in energy resource management.
📄 Additional Context:
Integrating weather data (temperature, humidity, etc.) with historical energy consumption data can significantly enhance the prediction accuracy by capturing key factors that influence energy demand.
Open Source Program: GSSoC'24-Extd
Note:
Please review the project documentation and ensure your code aligns with the project structure.
Please ensure that either the predict.py file includes a properly implemented model_details() function or the notebook contains this function to print a detailed model report. The model will not be accepted without this function in place, as it is essential for generating the necessary model details.
Prefer using a new branch to resolve the issue, as it helps keep the main branch stable and makes it easier to manage and review your changes.
Strictly use the pull request template provided in the repository to create a pull request.
🔍 Problem Description: I am working on a project to predict energy consumption for a building or city based on historical energy consumption data, weather conditions (temperature, humidity, etc.), occupancy, and usage patterns. The goal is to improve energy management and optimization by accurately forecasting future energy consumption.
🧠 Model Description: I plan to use time series forecasting models such as LSTM (Long Short-Term Memory) or ARIMA (AutoRegressive Integrated Moving Average). These models are well-suited for capturing temporal patterns in energy consumption data. LSTM can handle complex dependencies over time, while ARIMA is useful for trend and seasonality detection in energy usage.
⏲️ Estimated Time for Completion: 5 days to gather data, implement the model, and perform testing and validation.
🎯 Expected Outcome: The model is expected to provide accurate predictions of future energy consumption, helping to optimize energy usage, reduce costs, and improve sustainability efforts. The outcome will also support more effective decision-making in energy resource management.
📄 Additional Context: Integrating weather data (temperature, humidity, etc.) with historical energy consumption data can significantly enhance the prediction accuracy by capturing key factors that influence energy demand.
Open Source Program: GSSoC'24-Extd
Note:
Please review the project documentation and ensure your code aligns with the project structure. Please ensure that either the predict.py file includes a properly implemented model_details() function or the notebook contains this function to print a detailed model report. The model will not be accepted without this function in place, as it is essential for generating the necessary model details. Prefer using a new branch to resolve the issue, as it helps keep the main branch stable and makes it easier to manage and review your changes. Strictly use the pull request template provided in the repository to create a pull request.