Title : A comparison study of predictive models for electricity demand in a diverse urban environment

Objectives
Compare electricity demand prediction models across diverse urban environments.
Evaluate the accuracy and efficiency of models for different urban structures and behaviors.
Identify the most suitable models for smart energy management in cities.

Strategies and Methods
Model Selection: Use machine learning algorithms (e.g., neural networks, random forests) and statistical models (e.g., ARIMA).
Historical Data Analysis: Train models using electricity consumption, behavioral patterns, and environmental conditions.
Evaluation Metrics: Apply metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to compare model performance.
Scenario Simulation: Assess model performance under variable urban conditions, such as seasonal changes or population growth.

Applications
Predict electricity demand for urban infrastructure planning.
Optimize energy distribution to reduce waste in cities.
Support smart city development with a focus on energy sustainability.
Identify electricity consumption patterns across different urban areas.

Models and Algorithms
Deep Neural Networks (DNN): Detect complex patterns in consumption data.
Linear and Statistical Models: ARIMA and multiple regression for short-term forecasting.
Random Forest: Analyze the impact of various factors on electricity demand.
Support Vector Machines (SVM): Handle imbalanced datasets for accurate predictions.

Results
Identify the best-performing models for electricity demand prediction.
Provide solutions to enhance forecasting in diverse urban settings.
Improve accuracy in energy resource planning and distribution.
Help reduce costs and enhance sustainability in energy management.

Challenges and Limitations
Complexity of forecasting in diverse urban environments with varying consumption behaviors.
Need for high-quality, extensive data for accurate modeling.
Time and cost of training and implementing advanced models.
Sensitivity to unpredictable factors like crises or sudden climate changes.

Conclusion
A comparative study of electricity demand models offers valuable insights for selecting optimal methods in urban settings. These models can improve energy efficiency and support smart city development.

Future Work
Explore hybrid models to enhance prediction accuracy.
Study the impact of behavioral and social data on electricity demand forecasting.
Develop models capable of real-time energy data processing.
Implement and evaluate proposed models in geographically diverse cities.