This Document contains code and data for predicting electricity demand using SARIMA, LSTM, and Deep LSTM models.
The dataset used for this project is stored in a CSV file named electricity_demand.csv. It contains historical data of electricity demand along with timestamps.
The EDA and visualization code can be found in the Jupyter Notebook named EDA_and_Visualization.ipynb. This notebook explores the dataset, analyzes its features, and visualizes trends and patterns in the electricity demand data.
###SARIMA (Seasonal Autoregressive Integrated Moving Average)
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SARIMA, or Seasonal Autoregressive Integrated Moving Average, is a time series forecasting model that extends the ARIMA model to account for seasonality in the data. Here's a brief breakdown of its components:
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Seasonal Autoregression (AR): Models the relationship between an observation and a number of lagged observations in the seasonal period.
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Seasonal Integration (I): Represents the difference in the series at a particular lag, which helps to make the series stationary.
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Seasonal Moving Average (MA): Accounts for the error terms in the seasonal period. SARIMA models are particularly useful for time series data with clear seasonal patterns, such as electricity demand data.
###LSTM (Long Short-Term Memory)
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LSTM, or Long Short-Term Memory, is a type of recurrent neural network (RNN) architecture designed to capture long-term dependencies in sequential data. Here are its key features:
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Memory Cells: LSTM networks contain memory cells that can maintain information over long periods of time, allowing them to learn from sequences with long-term dependencies.
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Gate Mechanisms: LSTMs utilize gate mechanisms, including input gates, forget gates, and output gates, to regulate the flow of information within the network, enabling better gradient flow during training.
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Avoiding Vanishing Gradient Problem: By controlling the flow of information through the gates, LSTMs can mitigate the vanishing gradient problem often encountered in traditional RNNs.
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LSTM models have shown significant success in various sequence prediction tasks, including time series forecasting.
###Deep LSTM (Deep Long Short-Term Memory)
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Deep LSTM extends the basic LSTM architecture by adding multiple layers of LSTM units. By stacking multiple LSTM layers, deep LSTM models can learn increasingly complex representations of the input sequence. Here are some key aspects of deep LSTM models:
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Hierarchical Representation Learning: Each additional layer in a deep LSTM model learns hierarchical representations of the input data, enabling the model to capture intricate patterns and relationships.
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Feature Abstraction: Deep LSTM models can automatically extract relevant features from the input sequence at multiple levels of abstraction, leading to improved predictive performance.
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Computational Intensity: Training deep LSTM models can be computationally intensive, especially with a large number of layers and units. However, they often yield superior results compared to shallow architectures.
Predicting electricity demand can be beneficial in various domains, including:
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Energy Management: Utilities and energy companies can use accurate demand predictions to optimize energy generation, distribution, and pricing, leading to cost savings and improved resource utilization.
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Infrastructure Planning: Governments and city planners can leverage demand forecasts to plan infrastructure upgrades and expansions, such as building new power plants or upgrading transmission lines, to meet future demand effectively.
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Renewable Energy Integration: Predicting electricity demand helps in the efficient integration of renewable energy sources like solar and wind power, enabling better management of fluctuations and ensuring reliable power supply.
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Smart Grids: Smart grid systems can use demand predictions to dynamically adjust energy distribution, manage peak loads, and promote energy efficiency initiatives.
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Industrial Applications: Industries with high energy requirements, such as manufacturing and production facilities, can optimize their operations by aligning production schedules with predicted demand patterns, reducing energy costs and improving productivity.
To apply the prediction models, you need the following dependencies:
- Python 3
- pandas
- numpy
- statsmodels
- scikit-learn
- TensorFlow (for LSTM and Deep LSTM models)
Ensure you have the necessary dependencies installed in your environment.
To apply the prediction models, load the trained model weights and follow the steps outlined above. You may also need to adjust the code to suit your specific use case, such as preparing input data and post-processing predictions.