Electricity Demand Forecasting

 Electricity Demand Forecasting 

1. Introduction

Objective: Forecast electricity demand using historical data, seasonal decomposition, and machine learning models.
Purpose: Aid utility companies in efficient energy management and load planning by leveraging data-driven forecasting.

2. Project Workflow

1. Problem Definition:
   - Predict electricity demand using historical consumption data.
   - Key questions:
     - What are the seasonal patterns in electricity demand?
     - How accurately can we forecast future demand?
2. Data Collection:
   - Source: Public electricity consumption datasets, utility records, or APIs.
3. Data Preprocessing:
   - Handle missing values, outliers, and format time series data.
4. Exploratory Data Analysis:
   - Analyze trends, seasonality, and residuals using decomposition.
5. Modeling:
   - Use ML models (e.g., ARIMA, Random Forest, LSTM) for demand forecasting.
6. Evaluation and Insights:
   - Evaluate the model's performance and provide actionable insights.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
  - Data Handling: Pandas, NumPy
  - Visualization: Matplotlib, Seaborn, Plotly
  - Time Series Analysis: statsmodels, scikit-learn
  - Machine Learning: TensorFlow/Keras, XGBoost

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy matplotlib seaborn statsmodels scikit-learn tensorflow xgboost
```

Step 2: Collect and Explore Data

Load electricity consumption data:
```
import pandas as pd

data = pd.read_csv("electricity_demand.csv", parse_dates=['Timestamp'], index_col='Timestamp')
print(data.head())
```
Inspect the dataset:
```
print(data.info())
print(data.describe())
```

Step 3: Perform Seasonal Decomposition

Decompose the time series to analyze trend, seasonality, and residuals:
```
from statsmodels.tsa.seasonal import seasonal_decompose

decomposition = seasonal_decompose(data['Demand'], model='additive')
decomposition.plot()
plt.show()
```

Step 4: Preprocess Data for ML Models

Create features and labels for ML models:
```
data['Hour'] = data.index.hour
data['Day'] = data.index.day
data['Month'] = data.index.month

X = data[['Hour', 'Day', 'Month']]
y = data['Demand']
```
Split data into training and testing sets:
```
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```

Step 5: Train Machine Learning Models

Train a Random Forest model:
```
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

model = RandomForestRegressor()
model.fit(X_train, y_train)

predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f'Mean Squared Error: {mse}')
```

Step 6: Visualize Forecasts

Plot actual vs. predicted demand:
```
import matplotlib.pyplot as plt

plt.plot(y_test.values[:100], label='Actual')
plt.plot(predictions[:100], label='Predicted')
plt.title('Electricity Demand Forecasting')
plt.legend()
plt.show()
```

5. Expected Outcomes

1. Seasonal decomposition plots showing trends and patterns in electricity demand.
2. A machine learning model capable of accurately forecasting electricity demand.
3. Insights into demand patterns and recommendations for energy management.

6. Additional Suggestions

- Use advanced models like LSTM or Prophet for more accurate forecasts.
- Incorporate external factors (e.g., temperature, holidays) for improved predictions.
- Build a dynamic dashboard to monitor real-time forecasts and trends.