The document outlines a project aimed at developing a predictive model for forecasting energy load demand based on historical data, assisting utility providers in balancing supply and demand. It covers methodologies including multiple linear regression, neural networks, and time series forecasting, with a comparison of different models and their predictive accuracy. The final recommendations suggest using an MLR and ARIMA model for short-term forecasting and a neural network-based model for long-term predictions.

1. LOAD DEMAND
FORECASTING
AMAN MEHRA
ASHWIN MYSORE
DHRUV VARMA
SUHAS KASAR
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2. AGENDA
• Objective
• Methodology
• Data Exploration : Visualization
• Data Mining
• Analysis : Multiple Linear Regression
• Analysis : Neural Networks
• Analysis : Regression Trees
• Time Series Forecasting
• Analysis : Model Based Forecasting
• Analysis : Data Based Forecasting
• Final Model Selection
• Recommendation
• Q&A
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3. To develop a predictive model to forecast energy load demand
based on historical load data.
BUSINESS APPLICATION
This analysis will help utility providers (like Nstar) to balance the
grid, in terms of demand and supply. This is a constant problem
faced by them, due to the difficulty of storing energy.
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OBJECTIVE

4. • Source: kaggle.com.
• Load History: hourly load data (in Kw) for 20 zones.
• Time period: January 1st , 2004 to June 29th 2008.
• Temperature history: hourly temperature data from 11
stations.
• Relation between load zones and temperature stations not
provided.
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DATASET

5. METHODOLOGY
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7. Create a model to predict load, with good predictive ability as
indicated by:
• Predictor variables
• Low Root Mean Squared Error (RMSE)
• Low Mean Absolute Percentage Error (MAPE)
• Low Mean Absolute Deviation (MAD)
TOOLS
• Tableau : for Visualization and Data Exploration.
• MS Excel : Data cleaning.
• XL Miner: Data preparation, Partitioning, and Predictive analysis.
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PROPOSED SOLUTION

8. DATA EXPLORATION:
VISUALIZATION
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ZONE COMPARISON

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TIME – LOAD SCATTERPLOTS

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TIME – LOAD SCATTERPLOTS

12. DATA MINING
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13. MULTIPLE LINEAR REGRESSION
• Exhaustive Search
• Stepwise Regression
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Training Data scoring
Total SSE RMS Error Average Error
2.68472E+12 37560.3882 -0.00287194
Validation Data scoring
Total SSE RMS Error Average Error
1.80452E+12 37709.43569 -262.536391
18 predictors:
• Peak
• Weekday
• Month
• Year (indexed from 2004)
• Temperature (PC)
Predicted variable:
• Load

14. NEURAL NETWORK
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15. REGRESSION TREE
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Model 1
Model 4

16. TIME SERIES FORECASTING
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17. MODEL BASED FORECASTING
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18. MULTIPLE LINEAR REGRESSION
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19. AUTO CORRELATION
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20. ARIMA
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21. USING HISTORICALDATAIN VALIDATION
• Need for accuracy in load prediction increases closer to time.
• Used actual historical values rather than forecast.
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22. MOVING AVERAGE
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23. EXPONENTIAL SMOOTHING
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24. DATA BASED FORECASTING
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25. FINAL MODEL SELECTION
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26. TIME SERIES MODEL COMPARISON
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MAPE MAD RMSE MAPE MAD RMSE MAPE MAD RMSE MAPE MAD RMSE MAPE MAD RMSE
Overall 9.3% 20282.289 26955.474 10.3% 22822.534 29402.68 11.8% 25824.63 32960.324 14.9% 32894.727 41046.389 15.0% 33037.034 41178.493
Training 9.2% 19813.529 26419.515 10.2% 22215.242 28640.744 11.6% 25076.602 32016.909 14.6% 31821.683 39590.817 14.7% 31963.143 39717.925
Validation 9.4% 21844.821 28669.73 10.7% 24846.841 31810.934 12.2% 28318.057 35926.566 15.7% 36470.074 45561.936 15.7% 36615.205 45709.475
Previous Value Regression +previous residualARIMA Moving Average Exponential Smoothing
Comparison of Holt-Winter’s and secondary modeling combined models
MAPE MAD RMSE MAPE MAD RMSE MAPE MAD RMSE MAPE MAD RMSE MAPE MAD RMSE
Overall 14.0% 29096.732 46952.112 11.1% 23612.089 33612.567 17.6% 36363.833 62105.579 14.9% 32894.727 41046.389 13.3% 27786.061 39603.567
Training 8.3% 17343.106 22809.551 8.6% 17950.258 23664.047 9.0% 18580.25 24266.753 14.6% 31821.683 39590.817 9.6% 19820.69 25696.232
Validation 32.8% 68275.483 88422.762 19.6% 42484.858 55038.137 46.4% 95642.442 121454.5 15.7% 36470.074 45561.936 25.7% 54326.416 67781.677
ARIMA Moving Average ExponentialSmoothing Previous Value Regression +previous residual
Comparison of MLR and secondary modeling combined models

27. FINAL SELECTION
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Regression+ARIMA Regressionwith
temperature
Neural networkwith
temperature
Training
RMSE 26,420 37,560 31,059
MAPE 9.2% 13.80% 11.96%
MAD 19,814 29,347 24,658
Validation
RMSE 28,669 37,709 33,778
MAPE 9.4% 13.63% 12.64%
MAD 21,845 29.125 26,257

28. RECOMMENDATION
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• Use Time-Series based (MLR + ARIMA
order 2) for short-term
• Use Neural Networks based model for
long-term
RECOMMENDATION