DevDay2023 - Optimizer Power Use

Problem statement

• Unlike other renewable energy like coal, oil and gas, electric energy is hard to store at large scale and expensive at small scale. Thus, it must be consumed in real-time as it is generated. The commercial energy company will struggle to provide to end-users with safe and stable energy. Therefore, it is important to avoid significantly exceeding the required amount of electricity.
• Igrid Solution - a Japanese company provide services for energy consumers that want to handle that problem. So forecasting power generation and power demand at grid is great choice.

Data Description

• Dataset (Data granularity: Half Hour):

  • Target features: Power Generation, Power demand
  • Time varying known features: Time, date
  • Time varying unknown features: weather, solar and cloud intensity

• Metric to evaluate: (The judges called it Residual Mean Ratio Metric)

  $ RMR = \frac{sum(pred - actual)}{sum(actual)} $

• The dataset is confidential so i can't public it.

Preprocessing

• Add Features:
  • Date -> Year/Month/Day and day of the week
  • Half hours from start
• Normalization:
  • Numerical: Group Normalizer
  • Categorical: NaNLabelEncoder
• Outlier:
  • Exponential Smoothing
  • Kalman Smoothing
• Imputation Techniques:
  • KNN
  • Chained Equation (MICE)

Prepare Dataset

• Train set: From 25/03/2022 to 31/01/2023
• Validation set: From 01/02/2023 to 28/02/2023
• Test set: From 01/03/2023 to 31/03/2023
• Max Encoder Days: 30 days * 48 half hours in each day before the target day

Models

• RNN, DeepAR, Temporal Fusion Transformer. They are implemented in Pytorch-forecasting.

Bechmark

• Get high values at some cases when predicted values are higher than double of actual values. Because there is a day in validation set that grid doesn't generate the energy because of reasons like bad weather, eletric cut, etc...

References

• Pytorch-forecasting
• tsmoothie