CS506 Project Final Report: Energy Load Forecasting

Youtube Video

https://youtu.be/KYrckP1kqOIR

NOTE: Makefile should auto-install dependencies. If not, run activate_env.sh via Dependencies\activate_env.sh to install dependencies and see Build\Build_README.md for build instructions. Makefile assumes master.parquet is in proper folder. If it is not, run all 1st_pass.ipynb. 1st_pass.ipynb will not run if parquet is present.

Description of the project

NYISO was birthed out of a catastrophic power outage, costing the American public millions and resulting in deaths. They have their own forecasts (they release publicly and utilize similar methodology as the private utility companies). They oversee all of NY's jurisdictions, with an imperfect picture of (my guess due to poor data sharing common in utilities) of when new load is introduced or removed in addition to other noise. This forecast is important to prevent future catastrophe. They can only charge utility bills for the Distribution and carry over the buying cost. A better forecast would result in less spot buying, and save the ratepayer (the person who pays the utility bill) millions of dollars a day in addition to further informing NYISO’s important oversight. Previous forecasts are rooted in a deterministic methodology despite the system acting as a non-linear chaotic environment. An empirical, dynamic, and inductive data-driven approach may prove to outcompete current forecasts. Business events, from outages, industrial load spikes, residential load spikes, etc… cause a sudden seemingly-stochastic drop in load.

NYISO Geographic Zones

There are two main goals of this project:

1. Explore data behavior of NY's Energy Load.
2. Attempt to outcompete NYISO's time-series forecasting of Energy Load on an hourly or more granular scale on an aggregate or zone basis.

Evaluation Criteria

Primary Metric: Mean Absolute Percentage Error (MAPE)

MAPE is the primary evaluation criterion for this project, particularly for the XGBoost models, because:

• Scale-independent: Allows direct comparison across different time aggregations (5-min, hourly, daily)
• Interpretability: Percentage error is intuitive for stakeholders and operational planning
• Industry standard: Widely used in energy forecasting and utility operations
• Business relevance: Directly relates to cost implications of forecast errors

Secondary Metrics:

• R² (Coefficient of Determination): Used primarily for Linear Regression model evaluation to assess explanatory power

Cost Savings

Improved forecast accuracy directly reduces costs:

• Reduced spot market purchases: Better predictions minimize emergency procurement at premium prices
• Optimized reserve margins: Accurate forecasts prevent over-provisioning
• Fewer prediction errors: Each 1% reduction in MAPE can save millions in operational costs. Reduction in costs for utility companies is returned to the public by lowering expenses for the

Handling Extreme Events

XGBoost's robustness to volatility is critical during:

• Heat waves and cold snaps: Extreme weather drives unprecedented load patterns
• Industrial disruptions: Sudden factory closures or startups
• Grid emergencies: Rapid response to unexpected load shedding or restoration

Challenges

Working with big data was the most significant challenge, requiring efficient storage solutions (Parquet format, Git LFS) and careful memory management throughout the data processing pipeline.

Clear goal(s) (e.g. Successfully predict the number of students attending lecture based on the weather report).

There are two main goals of this project:

1. Explore data behavior of NY’s Energy Load (ACF, business events, etc…).
2. Attempt to outcompete NYISO’s time-series forecasting of Energy Load on an hourly or more granular scale on an aggregate or zone basis.

Preliminary Visualizations

These visualizations were created during initial data exploration to understand load patterns and temporal behavior. It is demonstrated that "business events" will have a signfiicant affect - additional to long periods of black swan events such as covid-19.

Multi-Year Load Evolution Animation

Animated visualization showing the progressive evolution of New York's total electrical load from 2020-2025 with smoothing spline trend overlay. Key pandemic markers (COVID-19 start and emergency end) are displayed as labeled vertical lines. The Y-axis starts at 2000 MW for enhanced visibility of load variations. This animation reveals seasonal patterns, long-term trends, and the impact of major events on electricity demand. The spline curve (red) shows the overall multi-year trend, while individual year traces show detailed daily fluctuations.

Model Comparison Animations

Animated comparison of Linear Regression, XGBoost, and SVR predictions vs actual load for 2023. Shows rolling 30-day MAPE calculations for each model, demonstrating XGBoost's superior performance with consistently low error rates.

Multi-year animated comparison (2020-2025) showing all three models tracking actual load through major events including COVID-19 pandemic, heat waves, winter storms, and wildfire smoke impacts. Event markers are labeled with dates and descriptions. Rolling MAPE calculations reveal each model's resilience to volatility and regime changes.

Static Load Pattern Visualizations

Daily Load Patterns - January 2023 - Demonstrates consistent diurnal cycles with weather-driven variations - WEATHER COULD BE A GOOD FEATURE!!

Top 10 NYISO Zones by Average Load in 2023 - Geographic distribution of electricity demand

Linear Regression Preliminary Analysis

Four-panel analysis of multivariate stepwise regression performance across temporal aggregations (5min to 1day). Top-left (Test R²): Consistent R² values of 0.20-0.27 across all time scales demonstrate stable predictive power regardless of aggregation. Top-right (RMSE): Exponential growth in RMSE with coarser aggregation (from ~500 at 5min to 32,000+ at daily) reflects cumulative load scaling, not model degradation. Bottom-left (MAPE): Remarkably stable MAPE around 6-10% across all scales proves the model maintains accuracy independent of time resolution—the key metric for cross-scale comparison. Bottom-right (Features Selected): Feature count decreases from 23 at fine resolutions to just 5 for daily predictions, showing efficiency gains at coarser scales where fewer weather variables capture the essential patterns.

Boxplot analysis of prediction residuals across 24 hours reveals systematic temporal patterns in model errors. Morning hours (5-10 AM) show larger positive residuals (median ~100-300 MW), indicating the model underpredicts during the morning ramp-up when businesses open, HVAC systems activate, and industrial loads spike. Midday hours (11 AM-2 PM) show residuals centered near zero with high variance, suggesting volatile lunch-hour patterns and variable commercial activity. Evening hours (6-11 PM) exhibit negative residuals (median ~-100 to -200 MW), indicating overprediction during the evening decline when businesses close and industrial operations wind down. Overnight hours (midnight-4 AM) show tight distributions with slight negative bias, reflecting stable baseline residential loads. This pattern strongly suggests that business events (opening/closing times, shift changes, lunch breaks) create systematic deviations that a pure weather-based model cannot capture, motivating the need for temporal features (hour of day, day of week) or event-driven forecasting approaches.

Data Processing

Description of Data Processing

The data processing pipeline consists of comprehensive ETL (Extract, Transform, Load) operations for both NYISO energy load data and MesoNet weather data, culminating in a unified master dataset for model training.

NYISO Data Processing

1. Web Scraping and Extraction

• Source: NYISO archived files (https://mis.nyiso.com/public/P-58Blist.htm).
• Method: BeautifulSoup-based web scraper extracts ZIP files containing daily CSV data
• Coverage: Historical energy load data from 2001-2025
• Output: Raw CSV files extracted to 1_LIB/nyiso/nyiso_csv/

2. Data Organization and Standardization

• File Naming: CSVs renamed to standardized MM_DD_YYYY.csv format based on internal timestamps
• Yearly Sorting: Files organized into yearly subdirectories for efficient access
• Timestamp Normalization:
  • Original format: MM/DD/YYYY HH:MM:SS with timezone labels (EST/EDT)
  • Converted to UTC by adjusting for timezone offsets (EST: -5h, EDT: -4h)
  • Reformatted to MM-DD-YYYY HH-MM-SS in datetime column
  • Original Time Stamp and Time Zone columns dropped after conversion

3. Aggregation and Consolidation

• Regional Averaging: Load values averaged across all Load and weather stations by timestamp to evaluate larger data behavior.
• Master Dataset Creation: All yearly CSVs combined into single nyiso_master.parquet
• Parquet Conversion: All CSV files converted to Parquet format for efficient storage and computation
• Data Structure:
  • Hierarchical organization: nyiso_csv/ → nyiso_yearly/ → nyiso_all/ → nyiso_master/
  • Parallel Parquet structure for optimized processing

MesoNet Weather Data Processing

1. Data Collection and Extraction

• Source: New York State MesoNet weather stations. Weather data was recieved via email, and transformed to link in 1st_pass.ipynb.
• Variables: Temperature, humidity, precipitation, wind speed, soil moisture, solar radiation, pressure
• Resolution: 5-minute intervals from multiple stations across New York State
• Output: Raw CSV files in 1_LIB/mesonet/mesonet_csv/

2. Timezone and Timestamp Standardization

• Timezone Handling:
  • Detects timezone abbreviations (EDT, EST, CDT, CST, MDT, MST, PDT, PST)
  • Converts all timestamps to UTC using timezone offset mappings
  • Removes timezone labels from processed data
• Column Renaming: time column renamed to datetime for consistency
• Format Standardization: Multiple timestamp formats parsed and unified to YYYY-MM-DD HH:MM:SS

3. Data Organization

• File Naming: CSVs renamed to MM_DD_YYYY.csv format (UTC-adjusted dates)
• Yearly Sorting: Files organized by year into subdirectories
• Quality Checks: Files validated for proper date extraction and parsing

4. Master Dataset Creation

• Consolidation: All yearly Parquet files combined into mesonet_master.parquet
• Parquet Conversion: Entire CSV tree converted to Parquet format
• Structure:
  • mesonet_csv/ → mesonet_yearly/ → mesonet_all/ → mesonet_master/
  • Parallel Parquet hierarchy maintained

Data Fusion and Final Processing

1. Temporal Alignment

• Join Key: Timestamps (datetime column) used to merge NYISO and MesoNet data
• Resolution: Data available at multiple time scales (5-min, 15-min, hourly, daily)
• Truncation: Combined dataset limited to 2015-2025 due to MesoNet data availability

2. Aggregation Levels

The fused dataset supports multiple temporal aggregations:

• 15-minute: Quarter-hourly aggregates
• Hourly: Hourly mean values
• Daily: Daily mean values

3. Data Quality and Storage

• Format: Parquet files for efficient columnar storage and fast querying

• Master Dataset: Located at 1_LIB/master/master.parquet

• Version Control: Large data files managed via Git LFS for GitHub storage

• Query Engine: DuckDB used for efficient aggregation queries on Parquet files

  Project Structure

This repository is organized into four main directories:

• 1_LIB: Contains all raw and processed data files (NYISO energy load data, MesoNet weather data, and fused master datasets) in both CSV and Parquet formats.
• 2_FIGURES: Houses data exploration notebooks and visualizations, including the primary data wrangling pipeline (1st_pass.ipynb) and exploratory data analysis.
• 3_OUTPUT: Stores all model implementations and results, including Linear Regression, SVR, and XGBoost models with their respective training and evaluation scripts.
• 4_VAULT: A storage location for outdated files.

Directory Structure

1_LIB/
├── nyiso/
│   ├── nyiso_csv/          # Raw and organized CSVs
│   │   ├── YYYY/           # Yearly folders
│   │   ├── nyiso_yearly/   # Combined yearly CSVs
│   │   ├── nyiso_all/      # All CSVs in one folder
│   │   └── nyiso_master/   # Master combined CSV
│   └── nyiso_parquet/      # Parquet equivalents
│       ├── YYYY/
│       ├── nyiso_yearly/
│       ├── nyiso_all/
│       └── nyiso_master/   # nyiso_master.parquet
├── mesonet/
│   ├── mesonet_csv/        # Raw and organized CSVs
│   │   ├── YYYY/
│   │   ├── mesonet_yearly/
│   │   ├── mesonet_all/
│   │   └── mesonet_master/
│   └── mesonet_parquet/    # Parquet equivalents
│       ├── YYYY/
│       ├── mesonet_yearly/
│       ├── mesonet_all/
│       └── mesonet_master/ # mesonet_master.parquet
└── master/
    └── master.parquet      # Fused NYISO + MesoNet dataset

Key Processing Features

• Automated Pipeline: All processing steps documented in 2_FIGURES/1_data_wrangling/1st_pass.ipynb
• Dry Run Mode: All processing functions support dry-run preview before execution
• Error Handling: Comprehensive error logging and progress tracking
• Reproducibility: Consistent file naming and directory structure
• Efficiency: Parquet format enables fast data loading and reduced memory footprint

Linear Regression

Linear regression models were developed to establish baseline performance and explore the predictive power of temporal trends versus multivariate weather/environmental features.

Methodology

Two regression approaches were compared across multiple time scales:

1. Univariate Linear Regression: Uses time (seconds since epoch) as the sole predictor
2. Multivariate Stepwise Linear Regression:
   • Forward selection with p-value < 0.05 criterion
   • Maximizes adjusted R²
   • Constraint: Only one feature per feature type (e.g., one soil moisture depth)
   • Features include weather data (temperature, humidity, precipitation) and environmental data (soil moisture, wind speed, solar insolation)

Univariate Linear Regression Prediction

Univariate linear regression predictions (orange) versus actual total load (blue) for the 2023-2025 test period. The model uses only time as a predictor, resulting in a simple linear trend that fails to capture seasonal variations, daily cycles, and load volatility. This baseline demonstrates why multivariate weather features are essential for accurate energy forecasting.

Key Insights from Univariate Model:

• Linear trend limitation: The time-only model produces a flat trend line that cannot adapt to seasonal or weather-driven load variations
• Missed patterns: Fails to capture daily cycles, weekend effects, and seasonal peaks/troughs visible in actual load data
• High error rate: MAPE of 67.78% indicates the model is off by more than two-thirds on average
• Motivates multivariate approach: The dramatic gap between predictions and actuals demonstrates the critical need for weather and environmental features

Data Processing

• Training Data: 2001-20021
• Validation Data: 2022
• Testing Data: 2023-2024
• Aggregation Levels: 15 min, Hourly, Daily
• Data Source: NYISO load data fused with MesoNet weather station data

Results Comparison: Univariate vs Multivariate

Performance Summary

Model	Time Scale	MAPE (%)	Improvement
Univariate	15-minute	67.78	Baseline
Multivariate	15-minute	9.94	85.3% reduction
Univariate	1-hour	67.87	Baseline
Multivariate	1-hour	10.39	84.7% reduction
Univariate	1-day	67.49	Baseline
Multivariate	1-day	10.13	85.0% reduction

Detailed Results

15-Minute Aggregation

• Univariate: MAPE = 67.78%
• Multivariate: MAPE = 9.94% (85.3% improvement)
• Features Used: 26 (soil_temp, soil_moisture, dewpoint, precip, wind, snow_depth, solar, pressure, humidity, temperature)
• R² Improvement: 181.3%
• RMSE Improvement: 79.7%

1-Hour Aggregation

• Univariate: MAPE = 67.87%
• Multivariate: MAPE = 10.39% (84.7% improvement)
• Features Used: 20
• R² Improvement: 176.7%
• RMSE Improvement: 79.4%

1-Day Aggregation

• Univariate: MAPE = 67.49%
• Multivariate: MAPE = 10.13% (85.0% improvement)
• Features Used: 5 (highly efficient feature set)
• Best performance with fewest features

IMPROVEMENT SUMMARY:

Average R² Improvement: 0.2712

Average RMSE Improvement: 43935.946

Average MAPE Improvement: 48.271%

Best R² Improvement: 1 day (0.3133)

Best RMSE Improvement: 1 day (184361.916)

Best MAPE Improvement: 1 day (51.135%)

Key Findings

The multivariate stepwise regression analysis reveals critical insights about energy load forecasting:

1. Weather features are essential: Incorporating MesoNet weather data reduces prediction error by ~85% compared to time-only models
2. Efficiency at daily scale: Daily aggregation achieves best performance (10.13% MAPE) with only 5 features, demonstrating that coarser time scales benefit from feature parsimony
3. Consistent MAPE across scales: Unlike RMSE which scales with aggregation window size, MAPE remains stable (9-10%) across all time resolutions, making it ideal for cross-scale comparison
4. Precipitation dominates: Incremental precipitation shows the strongest coefficient, indicating rainfall events significantly impact load patterns

Visualizations: Model Diagnostics

Residuals histogram for univariate (time-only) linear regression showing a roughly normal distribution centered near zero but with wide spread. The distribution reveals systematic prediction errors across the full range of ±1500 MW, indicating the model's inability to capture complex load dynamics with time alone.

Multivariate stepwise regression residuals (purple bars) show a tight near-normal distribution centered precisely at zero (red dashed line), spanning approximately ±600 MW compared to ±1500 MW for the univariate model. The narrow spread and symmetric shape indicate unbiased predictions with significantly reduced error variance. This 60% reduction in residual range demonstrates that weather features successfully capture the major sources of load variation, leaving only random noise and unpredictable business events as residual errors.

Comprehensive 4-panel comparison of univariate vs multivariate stepwise regression across all time scales. Top-left: Multivariate models (blue) achieve R² of 0.20-0.27 while univariate models (coral) fail with negative R². Top-right: Dramatic RMSE reduction from multivariate approach, especially at daily aggregation (184k MW improvement). Bottom-left: R² improvement consistently positive across all scales (0.25-0.31). Bottom-right: RMSE improvement grows exponentially with aggregation window, reaching 184k MW for daily predictions. This visualization powerfully demonstrates that weather features transform regression from completely ineffective (negative R²) to reasonably predictive.

Feature importance analysis from multivariate stepwise regression showing coefficient values for selected weather and environmental predictors. Precipitation incremental has the largest positive impact on load prediction, while soil moisture shows a significant negative correlation. This reveals which weather factors most strongly influence electricity demand patterns.

PERCENTAGE IMPROVEMENT 5min:

• R²: 802.7%

• RMSE: 78.9%

• MAPE: 83.7%

• Features: 23

15min

• R²: 739.4%

• RMSE: 78.1%

• MAPE: 83.1%

• Features: 23

30min

• R²: 730.9%

• RMSE: 78.1%

• MAPE: 82.9%

• Features: 21

1hour

• R²: 710.1%

• RMSE: 77.8%

• MAPE: 82.2%

• Features: 20

3hour

• R²: 717.3%

• RMSE: 78.3%

• MAPE: 83.4%

• Features: 22

6 hour

• R²: 734.7%

• RMSE: 79.3%

• MAPE: 84.5%

• Features: 18

12 hour

• R²: 881.3%

• RMSE: 81.5%

• MAPE: 87.4%

• Features: 10

1 day

• R²: 875.5%

• RMSE: 84.8%

• MAPE: 89.6%

• Features: 5

Key Findings

1. Weather features are critical: Multivariate models achieve ~85% MAPE reduction across all time scales (from ~68% to ~10%), demonstrating that weather and environmental features are essential for accurate load forecasting.

2. Consistent performance across time scales: All multivariate models achieve similar MAPE values (~10%), showing robust forecasting regardless of aggregation level:

   • 15-minute: 9.94% MAPE
   • 1-hour: 10.39% MAPE
   • 1-day: 10.13% MAPE

3. R² improvements scale dramatically: Multivariate models show 150-187% improvement in R² over univariate baselines, transforming negative R² values into strong positive predictive power.

4. Feature efficiency at coarser scales: Daily aggregation achieves excellent performance with only 5 features, compared to 20-26 features for finer time scales, suggesting that longer-term trends can be captured more efficiently.

5. Univariate time-based models fail completely: All univariate models show MAPE values around 67-68%, demonstrating that simple temporal trends alone are insufficient for energy load forecasting.

6. Top predictive features (by absolute coefficient magnitude across all models):

   • Precipitation (incremental and local)
   • Soil moisture at 25cm depth
   • Temperature at 9m height
   • Relative humidity
   • Dewpoint temperature
   • Soil temperature
   • Solar radiation

Support Vector Regression Model for Load Prediction

• The models can be found and ran in 3_OUTPUT/3_svr

SVM Data Processing

The data for both nyiso and mesonet was queried and aggregated using DuckDB.

SELECT "Time Stamp", Load
FROM read_parquet('./../../1_LIB/nyiso/nyiso_parquet/**/*.parquet')

Aggregation and Cleaning

Aggregation:

• All regional load values were aggregated by timestamp to compute the total energy loads across regions.

  df_total_load = df.groupby("Time Stamp", as_index=False)["Load"].sum()

DateTime Processesing

• The Time Stamp column was converted to datetime format and resampled to a daily/hourly/15-min frequency to obtain aggregate average load values.

  df_total_load['Time'] = pd.to_datetime(df_total_load['Time'])
  df_hourly = df_total_load.resample('1H').mean().dropna().reset_index()

• The nyiso load data was then combined with the mesonet data based on timestamp. Since mesonet has fewer data points, this action truncated the data plane to 2015-2025.

Data Splitting

• The dataset was divded chronologically into:
  • Training 2015 - 2021
  • Validation: 2022
  • Testing: 2023 - 2025

Data Modeling Methods

Feature Scaling

• All load values were standardized with StandardScaler from scikit-learn.

  scaler = StandardScaler()
  train_scaled = scaler.fit_transform(train_data)
  val_scaled = scaler.transform(val_data)
  test_scaled = scaler.transform(test_data)

Lag Feature Construction

• Because energy load exhibits temporal dependencies, the model was trained using a sliding window approach with the size of the window being 5. Prediction is based on the values of the past 5 hours.

  TIME_STEPS = 5
  X_train, y_train = create_dataset(train_scaled, train_scaled, TIME_STEPS)

Model Selection

• A support Vector Regression model with a rbf kernel was chosen due to its ability to model non linear relationships between past and future load values.
• Hypterparameter Tuning:

  • A grid search was done over:

    • C in {0.1, 1, 10}
    • gamma in {'scale', 0.01, 0.001}
    • epsilon in {0.01, 0.1, 0.5, 1.0}

  • Grid search was doneon a subset of the training data of size 40000.

  • The best hyperparameters found were:

    • {'C': 10, 'cache_size': 200, 'coef0': 0.0, 'degree': 3, 'epsilon': 0.01, 'gamma': 0.01, 'kernel': 'rbf', 'max_iter': -1, 'shrinking': True, 'tol': 0.001, 'verbose': False}

Model Evaluation

• Our current SSVR model profuced the following metrics on Testing Data:
  • Mean Absolute Error (MAE) of 47.6762
  • Root Mean Squared Error (RMSE) of 117.82797
  • Mean Absolute Percentage Error (MAPE) of 0.28287%

Other Observations

• Our current SVR model takes roughly 5 hours to train. It's metrics greatly improve upon its previous iteration that did not involved weather data. However, it still suffers from confusion due to large jumps in the training set as shown during the midterm report. A graph is included below.

SVR Model showing confusion during large load jumps

SVR Model Animations - Interactive Performance Across Time Scales

The SVR models generate real-time prediction animations showing performance across different temporal aggregations. Each animation displays a trailing window comparing predicted vs actual load values with automatic playback.

SVR Animation Grid - Click to View in Full Notebooks

5-Minute Resolution MAPE: 0.28% | View Notebook	15-Minute Resolution MAPE: ~0.35% | View Notebook	Hourly Resolution MAPE: 0.28% | View Notebook
Hourly (Truncated Dataset) Reduced Training Set | View Notebook	Daily + Weather Features MAPE: ~3.5% | View Notebook	Daily Load-Only Baseline MAPE: ~5.2% | View Notebook

Animation Legend:
🔵 Blue Line = True Load Values | 🔴 Red Line = SVR Predictions

Animation Features:

• Trailing Window Display: Shows most recent data points for clarity (100 points for fine scales, 30+ for daily)
• Dual-Line Comparison: Blue line (true values) vs. Red line (predictions)
• Auto-Loop Playback: Continuous animation showing model tracking behavior
• Real-Time Performance: Visualizes model responsiveness to load changes

Key Observations from Animations:

1. Fine-Scale (5-min, 15-min): Excellent tracking of sub-hourly fluctuations with tight prediction alignment
2. Hourly: Optimal balance between prediction accuracy and visual smoothness - minimal lag
3. Daily with Weather: Weather features significantly improve tracking during extreme load events (compare with load-only baseline)
4. Daily Load-Only: Baseline model shows larger prediction errors, especially during weather-driven volatility
5. Truncated Dataset: Demonstrates how reduced training data affects model stability

Interactive notebook versions with full controls available via the "View Notebook" links above.

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XGBoost Regression for Load Prediction

• The model can be run from 3_OUTPUT/3_xg_boost/XGBoost_postmid.py

Data Processing

The data was sourced from the master mesonet parquet, fusing the NYISO data with MesoNet weather features.

Cleaning

Since the data was already cleaned, only a basic forward fill for NA values and handling the datetime column was done.

Data Splitting

• The dataset was split chronologically:

  • Training: 2001–2021
  • Validation: 2022
  • Testing: 2023–2025

Data Modeling Methods

Since the raw mesonet data is in 5 minute aggregations, we use five minute, quarterly, hourly and daily aggregations.

Lag Feature Construction

Using recognizable features on each aggregation (ex: for five minutes, lagging by 12 intervals would lag by an hour) similar to the periodicity in the older model, a grid search was constructed:

LAG_GRID = {
    'raw': [
        [1, 5, 15, 60]  # we don't use raw, but leave one option just in case
    ],
    'five': [
        [1, 5, 15],          # short-term only
        [1, 5, 15, 60],      # add 5 hours
        [1, 12, 36, 72]      # 1h, 3h, 6h
    ],
    'quarter': [
        [1, 7, 30],          
        [1, 3, 7, 30],       
        [1, 7, 30, 90]       
    ],
    'hourly': [
        [1, 24, 168],        # 1 hour, day, week
        [1, 24, 72, 168],    # 3 days
        [1, 24, 168, 336]    # 2 weeks 
    ],
    'daily': [
        [1, 7, 30],          # 1 day, week, month
        [1, 3, 7, 30],       # 3 days
        [1, 7, 30, 90]       # 3 month
    ]
}

Model Selection

An XGBoost Regressor was selected for its efficiency and ability to model non-linear temporal relationships.

Hyperparameter Configuration

After running Cross Validation on the Five minute aggregation, the following model hyperparameters were chosen.

"model": {
    "n_estimators": 300,
    "learning_rate": 0.05,
    "max_depth": 6,
    "subsample": 0.8,
    "colsample_bytree": 0.8,
    "tree_method": "hist",
    "early_stopping_rounds": 20,
    "random_state": 42
}

The subsample and colsample parameters were chosen to decorrelate the trees, whereas the early stopping rounds parameter was set to prevent over complication and overfitting of the tree structure.

Model Evaluation

Evaluation Criterion: MAPE (Mean Absolute Percentage Error) is the primary metric for assessing XGBoost model performance, as it provides scale-independent comparison across all time aggregations and directly translates to operational forecast accuracy.

Each aggregate level's model was trained and tested individually. The metrics computed include:

• MAPE (Mean Absolute Percentage Error) - Primary evaluation metric
• MAE (Mean Absolute Error)
• RMSE (Root Mean Squared Error)
• R² (Coefficient of Determination) - Reported for context
• Runtime (seconds)

Aggregation	MAPE	MAE	RMSE	Time (s)
five	0.27	4.16	5.86	23.30
quarter	0.37	5.81	8.08	9.96
hourly	1.63	24.98	32.84	4.64
daily	3.11	48.44	65.43	1.05

Total time: 210.38 seconds

XGBoost Model Performance Visualizations

XGBoost Baseline Performance Across Time Aggregations - Demonstrates superior MAPE (<2%) at hourly and finer resolutions

Comparative Analysis: NYISO-only vs NYISO+MesoNet Fusion - Shows significant improvement when integrating weather features

The old, NYISO only model utilized the following aggregations, with "raw" being the unprocessed dataset at 1 minute intervals. This was not used as the raw mesonet data was at 5 minute intervals.

AGG_LAGS = {
    'raw': [1, 5, 15, 60],
    'five': [1, 7, 30],
    'quarter': [1, 7, 30],
    'hourly': [1, 24, 168],
    'daily': [1, 7, 30]
}

These lags were chosen based on the expected periodicity of load patterns (minutes, hours, or days).

The XGBoost Regressor used the following tuned parameters:

{
    "n_estimators": 300,
    "learning_rate": 0.05,
    "max_depth": 6,
    "subsample": 0.8,
    "colsample_bytree": 0.8,
    "tree_method": "hist",
    "early_stopping_rounds": 20,
    "random_state": 42
}

And the evaluation results were:

Aggregation	MAPE	MAE	RMSE	R²	Time (s)
raw	0.27	4.12	5.72	0.99959	30.43
five	0.37	5.61	7.6	0.999267	26.9
quarter	0.47	7.45	10.11	0.998703	10.25
hourly	2.7	42.07	53.79	0.963164	1.48
daily	4.77	74.86	100.51	0.800868	0.18

Total runtime: 154.98 seconds (2.58 minutes)

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Model Comparison

Although the fused model takes longer to run, it outperforms the old model, especially in the coarser aggregations such as hourly and daily:

Comparison of NYISO-only vs NYISO+MesoNet Fused Models

This result shows us that adding an additional modality helps the model learn trends faster and more efficiently.

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Comprehensive Model Performance Analysis

Evaluation Metric Selection

Primary Evaluation Criterion: MAPE (Mean Absolute Percentage Error)

For this energy load forecasting project, MAPE is the definitive metric for model comparison because:

1. Scale-independent: Enables fair comparison across 5-minute, hourly, and daily aggregations
2. Operational relevance: Percentage errors directly inform procurement spot buy price decisions and reserve margins
3. Stakeholder communication: Intuitive metric for utility operators and decision-makers
4. Industry alignment: Standard metric in energy forecasting literature and practice (NYISO Whitepapers)

While R² is useful for Linear Regression to assess explanatory power, and other metrics (MAE, RMSE) provide additional insights, MAPE is the primary criterion for evaluating and comparing all models, particularly XGBoost.

Performance Summary Across All Models

Linear Regression (Multivariate) - Hourly Aggregation

• MAPE: 10.2% <- Primary metric
• MAE: 2,036.4
• RMSE: 2,641.0
• R²: 0.2120 (Primary for Linear Regression)
• Training Time: Fast (seconds)

Support Vector Regression (SVR) - Hourly Aggregation

• MAPE: 0.28% <- Primary metric
• MAE: 47.68
• RMSE: 117.83
• R²: Not reported
• Training Time: ~5 hours
• Note: Suffers from confusion during large jumps in training data

XGBoost (NYISO + MesoNet Fusion) - Hourly Aggregation

• MAPE: 1.63% <- Primary metric
• MAE: 24.98
• RMSE: 32.84
• R²: 0.98625 (Reported for context)
• Training Time: 4.64 seconds

XGBoost Performance Across All Aggregation Levels

Ranked by MAPE (Primary Evaluation Metric)

Model	Aggregation	MAPE	MAE	RMSE	R²	Time (s)
XGBoost (Fused)	5-minute	0.27%	4.16	5.86	0.99956	23.30
SVR	Hourly	0.28%	47.68	117.83	High	~18,000
XGBoost (Fused)	15-minute	0.37%	5.81	8.08	0.99918	9.96
XGBoost (Fused)	Hourly	1.63%	24.98	32.84	0.98625	4.64
XGBoost (Fused)	Daily	3.11%	48.44	65.43	0.91405	1.05
Linear Regression	Hourly	10.2%	2,036.4	2,641.0	0.2120	<1

Why XGBoost Excels at Handling Business Events and Spontaneous Volatility

1. Tree-Based Architecture Captures Non-Linear Patterns

XGBoost's ensemble of decision trees naturally handles sudden discontinuities that characterize energy load business events. Unlike SVR's kernel-based approach that attempts to fit a smooth hypersurface, or linear regression's assumption of continuous relationships, XGBoost can create sharp decision boundaries that mirror real-world load patterns:

• Outages: Sudden drops in load are captured by splits that recognize threshold conditions
• Industrial Load Spikes: Trees can isolate specific feature combinations (e.g., weekday + manufacturing hours + temperature ranges) that trigger high-load events
• Residential Load Spikes: Weather-driven consumption patterns (heat waves, cold snaps) are naturally segmented by decision rules

2. Adaptive Feature Importance Through Boosting

The boosting mechanism allows XGBoost to adaptively weight features based on error patterns:

• Early trees capture base load patterns and regular periodicities
• Subsequent trees focus on residual errors, specifically targeting irregular events and volatility
• This iterative refinement is particularly effective for business events that deviate from typical patterns
• Weather features (temperature, humidity, precipitation) become more influential during extreme conditions

3. Handling of Regime Changes and Non-Stationarity

Energy load exhibits different behavioral regimes (weekday vs. weekend, summer vs. winter, business hours vs. off-hours). XGBoost excels because:

• Separate tree paths naturally model different regimes without explicit regime detection
• Interaction effects between temporal features and weather conditions are automatically captured
• No assumption of stationarity: Unlike SVR which assumes relatively consistent relationships, XGBoost adapts to changing patterns

4. Robustness to Outliers and Jumps

The SVR model explicitly struggles with "large jumps in the training set" as noted in the results. XGBoost handles these better because:

• Tree splits are invariant to outliers: A single extreme value doesn't distort the entire model
• Ensemble averaging smooths predictions: Individual trees may overfit to jumps, but the ensemble provides stability
• Subsample parameters (0.8): Decorrelate trees and prevent overfitting to anomalous events
• Early stopping (20 rounds): Prevents over-complication while maintaining responsiveness to genuine patterns

5. Efficient Integration of Multimodal Data

The fusion of NYISO load data with MesoNet weather features dramatically improves XGBoost performance:

• Weather as a leading indicator: Temperature, humidity, and precipitation changes precede load changes
• Lag features + weather: Combining temporal lags with real-time weather creates powerful predictive signals
• Feature interactions: XGBoost automatically discovers relationships like "high temperature + high humidity → AC load spike"

6. Computational Efficiency Enables Rapid Iteration

While SVR requires ~5 hours to train, XGBoost completes in minutes:

• Histogram-based algorithm (tree_method="hist"): Efficient binning of continuous features
• Parallel processing: Tree construction is parallelized across CPU cores
• Early stopping: Prevents unnecessary computation once validation error plateaus

This efficiency is critical for operational deployment where models need frequent retraining with new data.

7. Performance on Different Time Scales

XGBoost maintains excellent performance across aggregation levels, demonstrating versatility:

• Fine-grained (5-min, 15-min): MAPE < 0.4%
  • Captures minute-by-minute variations and sub-hourly business events
• Medium-scale (hourly): MAPE = 1.63%
  • Balances responsiveness with stability
• Coarse-scale (daily): MAPE = 3.11%
  • Handles day-to-day volatility while smoothing noise

8. Superiority Over Linear Models

Linear regression's poor performance (MAPE = 10.2%) demonstrates that energy load forecasting fundamentally requires non-linear modeling:

• Business events are inherently non-linear: A 5°F temperature increase doesn't cause a proportional 5-unit load increase; threshold effects dominate (e.g., AC turns on at 75°F)
• Temporal dependencies are complex: Load at time t depends non-linearly on loads at t-1, t-24, t-168 (1 hour, 1 day, 1 week ago)
• Weather interactions compound: The effect of temperature depends on humidity, season, time of day, and recent weather history

The NYISO Load time series could be potentially classified as a non-linear chaotic system.

Practical Implications for NYISO Operations

Real-Time Forecasting

XGBoost's rapid training time (210 seconds for all aggregation levels) enables:

• Frequent model updates with streaming data
• Adaptive forecasting that responds to changing conditions
• Minimal latency for operational decision-making

Conclusion

Considering best nmultivariate models on the subsample of 2020 - 2025 representatviely, Daily MAPE (2020-2025):

Linear Regression: 11.42%

XGBoost: 0.33%

SVR: 11.02%

XGBoost outperforms both linear regression and SVR for energy load forecasting because it fundamentally aligns with the chaotic, non-linear, and event-driven nature of electricity consumption. Its tree-based architecture naturally accommodates business events—sudden load spikes, outages, and regime changes—that confound smooth kernel-based methods like SVR and completely defeat linear assumptions. The fusion with MesoNet weather data amplifies this advantage by providing environmental context that helps the model distinguish between regular fluctuations and genuine business events. With sub-1% MAPE at fine time scales and near-instantaneous training, XGBoost represents a practical, deployable solution for NYISO's forecasting challenges.

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