To design a model that predicts energy consumption in smart cities and provides actionable insights to reduce wastage. This could involve optimizing energy distribution based on usage patterns and predicting future demand.
As urbanization accelerates and smart cities expand, managing energy consumption has emerged as a critical challenge, with significant implications for sustainability and environmental health. This project presents a predictive model that analyzes energy consumption patterns in smart cities to offer actionable insights aimed at reducing wastage. By integrating data from households, weather conditions, population demographics, and time-of-day usage, the model forecasts future energy demands and optimizes energy distribution. It identifies consumption trends, peak usage periods, and anomalies, providing targeted recommendations for energy-saving measures and efficient distribution strategies. This approach not only minimizes energy wastage but also contributes to lowering the carbon footprint in urban areas, supporting the development of sustainable smart cities and advancing global efforts to combat climate change.
To Design an intelligent system dashboard that leverages machine learning to predict energy consumption in smart cities. The system will analyse energy consumption data from households, weather patterns, population demographics, and time-of-day factors to identify usage trends, optimize energy distribution, and predict future energy demands.
We collected 4 different Datasets to work on, they are:
- Energy Consumption Data
- Weather Data
- Electricity Consumption Data
- Climate Change Data (CO2 Emissions) Data
The Project Design includes the following features:
- Energy Optimization & Prediction
- Electricity Consumption Visualisation
- Climate Change Prediction (CO2 Emissions)
The Insights gained from Ecosync are:
- Peak Demand Identification
- Predictive Maintenance
- Understanding CO2 Emissions & Global warming awareness
- IBM LinuxOne System - Virtual Jupyter Notebook
- Machine Learning : LSTM & GRU
- Visualization Tools: Plotly
- Web Technologies
- Integration: Flask
Ecosync aims to...
- Reduces energy wastage
- Promotes sustainability
- Lowers carbon footprint in urban areas
- Highlights: Energy consumption and optimization - SDG 07 & Industrial revolution 5.0