ASHRAE - Great Energy Predictor III - Top 5 winning solutions

This repository contains the code and documentation of top-5 winning solutions from the ASHRAE - Great Energy Predictor III cometition. It also contains comparative analysis of these solutions with respect to their characteristics such as workflow, computation time, and score distributation with respect to meter type, site, and primary space usage, etc.

First rank solution

• Code
• Sulution summary (Kaggle discussion board)

Second rank solution

• Code
• Solution summary (PDF)
• Sulution summay (Kaggle discussion board)

Third rank solution

• Code
• Solution summary (PDF)
• Sulution summay (Kaggle discussion board)

Fourth rank solution

• Code
• Solution summary (.DOCX)
• Sulution summay (Kaggle discussion board)

Fifth rank solution

• Code
• Solution summary (.DOCX)
• Solution summary presentation (PDF)
• Sulution summay (Kaggle discussion board)

Comparison

Final Rank	Team Name	Final Private Leaderboard Score	Preprocessing Strategy	Features Strategy Overview	Modeling Strategy Overview	Post-Processing strategy
1	Isamu & Matt	1.231	Removed anomalies in meter data and imputed missing values in weather data	28 features, Extensively focused on feature engineering and selected	LightGBM, CatBoost, and MLP models trained on different subsets of the training and public data	Ensembled the model predictions using weighted generalized mean.
2	cHa0s	1.232	Visual analytics and manual inspection	Raw energy meter data, temporal features, building metadata, simple statistical features of weather data.	XGBoost, LightGBM, Catboost, and Feed-forward Neural Network models trained on different subset of the training set	Weighted mean. (different weights were used for different meter types)
3	eagle4	1.234	Eliminated 0s in the same period in the same site	nan	Keras CNN, LightGBM and Catboost	nan
4	不用leakage上分太难了	1.235	Not available	23 features including raw data, aggregate, weather lag features, and target encoding. Features are selected using sub-training sets.	XGBoost (2-fold, 5-fold) and Light GBM (3-fold)	Ensembled three models. Weights were determined using the leaked data.
5	mma	1.237	Dropped long streaks of constant values and zero target values.	Target encoding using percentile and proportion and used the weather data temporal features	LightGBM in two steps -- identify model parameters on a subset and then train on the whole set for each building.	Weighted average.

Comparison of execution time

Solution	Preprocessing	Feature engineering	Training	Prediction	Ensembling	Total (minutes)
Rank 1	9	128	7440	708	35	8320
Rank 2	36	24	1850	94	7	2011
Rank 3	178	12	501	100	14	805
Rank 4	40	7	85	46	6	184
Rank 5	3	9	13	20	16	61

Note: all solutions were reproduced on AWS EC2 (g4dn.4xlarge) using Deep Learning AMI.

Links

1. Top 5 winning solutions - code and docs (original submission by the winners)
2. Top 5 winning solutions - explainer videos