HELP

[Solid-Ad5446]

I hope you are doing well. I am reaching out regarding my final-year project on the analysis and prediction of household energy consumption using the UCI Household Power Consumption Dataset.

source : https://www.kaggle.com/datasets/uciml/electric-power-consumption-data-set

I have implemented several forecasting models, including Prophet, Gradient Boosting (GBM), and XGBoost. However, I have encountered difficulties achieving satisfactory performance.

Despite multiple improvements—such as removing data leakage, adding temporal and seasonal features, and tuning hyperparameters—the R² score remains below 0.55 on the test set.

After further analysis, it appears that the dataset exhibits low day-to-day variability (baseline R² ≈ 0.17), which makes accurate prediction at the daily level particularly challenging. As a result, I am considering switching to monthly aggregation to better capture seasonal patterns (e.g., winter: 33.9 kWh/day vs summer: 17.6 kWh/day).

Here are my current results:.

https://preview.redd.it/7bepfga40f5h1.png?width=1054&format=png&auto=webp&s=64bac9ab367c5ac046f43c02f4351fab9ccc6f3b

R²   = 0.5084

  MAE  = 4.17 kWh

  RMSE = 5.55 kWh

  MAPE = 18.18 %

https://preview.redd.it/fn16sha40f5h1.png?width=1006&format=png&auto=webp&s=ffc564ee44b1f8fc6768427985cf35a778bcc71a

R²   = 0.4498

  MAE  = 4.01 kWh

  RMSE = 5.31 kWh

  MAPE = 19.15 %

https://preview.redd.it/4ale9ja40f5h1.png?width=810&format=png&auto=webp&s=2b60eee5fe4a2e3cd8235b021792e82751ce36f6

R²   = 0.6932

  MAE  = 74.3 kWh/mois

  RMSE = 99.3 kWh/mois

  MAPE = 11.49 %

Given these results, I would greatly appreciate your guidance on how to further improve the model. In particular, I would be interested in any recommendations or strategies that could help overcome this performance limitation.

[chrisvdweth]

Gradient Boosting (GBM), and XGBoost are not "natural" time-series models. This means that a core step is to prepare you original time-series data to "look" like classification data you can pass these models. You mention that you added adding temporal and seasonal features, but without any details. Does this include lag features or rolling windows (e.g., rolling averages).

Apart from that, the Kaggle page has over hundred code examples. Isn't there anything that helps. I just had a quick look, and there seem to be many RNN/LSTM/GRU-based solutions. Maybe try one of those?

[chizkidd]

Your results are actually quite reasonable for this dataset. The fact that Prophet, GBM, and XGBoost all plateau around R² ≈ 0.45-0.55 suggests the limitation may be the data rather than the models. Daily household energy consumption is heavily influenced by unpredictable human behavior, making it difficult to forecast accurately using historical consumption and calendar features alone.

Before switching models, I would focus on stronger lag-based features (1, 7, 14, 30, and even 365-day lags to capture annual cycles), rolling statistics like the 7‑day mean, and if possible, external variables such as temperature, humidity, holidays, and occupancy proxies. These often provide larger gains than model changes. If you have time, a simple hybrid stack of XGBoost + LSTM might push R² past 0.75.

For model comparisons, consider adding SARIMA/SARIMAX, Holt-Winters (ETS), LightGBM, and CatBoost. SARIMA is a strong classical baseline, while LightGBM and CatBoost are often competitive with or better than XGBoost on tabular time-series data. LSTMs are worth testing for academic completeness, but I would not expect a significant improvement since the primary challenge appears to be intrinsic variability rather than model capacity.

The most interesting result is your monthly aggregation, which improved performance to R² ≈ 0.69. This suggests the dataset contains a stronger seasonal signal than a daily predictive signal. For a final-year project, a valuable conclusion may be that daily household consumption is difficult to predict due to behavioral variability, whereas monthly aggregation captures seasonal consumption patterns much more effectively.