14 KiB
14 KiB
Different Model Architectures (For Quantile Regression)
Baseline
The baseline just calculates the values for the given quantiles using the training data.
Mean NRV for whole day
| train_CRPSLoss | test_CRPSLoss |
|---|---|
| 74.1899447775193 | 79.26462867583763 |
Auto Regressive Models
Linear Model
Example summary of the Linear Model
==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
Sequential [1024, 13] --
├─TimeEmbedding: 1-1 [1024, 195] --
│ └─Embedding: 2-1 [1024, 2] 192
├─LinearRegression: 1-2 [1024, 13] --
│ └─Linear: 2-2 [1024, 13] 2,548
==========================================================================================
Total params: 2,740
Trainable params: 2,740
Non-trainable params: 0
Total mult-adds (M): 2.81
==========================================================================================
Input size (MB): 0.79
Forward/backward pass size (MB): 0.12
Params size (MB): 0.01
Estimated Total Size (MB): 0.93
==========================================================================================
| Experiment | Quarter | Load forecast | Load History | test_L1Loss | test_CRPSLoss |
|---|---|---|---|---|---|
| Link | False | False | False | 105.62005737808495 | 78.6946345109206 |
| Link | True | False | False | 104.97209199411934 | 78.15958404541016 |
| Link | True | True | False | 104.98653461048444 | 78.18278430058406 |
| Link | True | True | True | 104.82491272720578 | 77.90755403958835 |
Quantiles Histogram Debug Plots
Non Linear Model
Example summary of the Non Linear Model
==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
Sequential [1024, 13] --
├─TimeEmbedding: 1-1 [1024, 96] --
├─NonLinearRegression: 1-2 [1024, 13] --
│ └─ModuleList: 2-9 -- (recursive)
│ │ └─Linear: 3-1 [1024, 512] 49,664
│ └─ReLU: 2-2 [1024, 512] --
│ └─ModuleList: 2-9 -- (recursive)
│ │ └─Dropout: 3-2 [1024, 512] --
│ └─ReLU: 2-4 [1024, 512] --
│ └─ModuleList: 2-9 -- (recursive)
│ │ └─Linear: 3-3 [1024, 512] 262,656
│ └─ReLU: 2-6 [1024, 512] --
│ └─ModuleList: 2-9 -- (recursive)
│ │ └─Dropout: 3-4 [1024, 512] --
│ └─ReLU: 2-8 [1024, 512] --
│ └─ModuleList: 2-9 -- (recursive)
│ │ └─Linear: 3-5 [1024, 13] 6,669
==========================================================================================
Total params: 318,989
Trainable params: 318,989
Non-trainable params: 0
Total mult-adds (M): 326.64
==========================================================================================
Input size (MB): 0.39
Forward/backward pass size (MB): 8.50
Params size (MB): 1.28
Estimated Total Size (MB): 10.16
==========================================================================================
| Experiment | Quarter | Load forecast | Load History | test_L1Loss | test_CRPSLoss |
|---|---|---|---|---|---|
| Link | False | False | False | 105.75275872112196 | 79.5905984731821 |
| Link | True | False | False | 104.9115321283131 | 78.9574656853309 |
| Link | True | True | False | 104.05637291829032 | 78.49674870417668 |
| Link | True | True | True | 103.89383283348461 | 77.7099763430082 |
Quantiles Histogram Debug Plots
LSTM/GRU Model
Input shape: (batch_size, sequence_length, input_features)
If load forecast is used as input, each time step gets the load forecast for the next time step as feature.
Example: We have time steps:
| 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|
| NRV | 0.1 | 0.2 | 0.15 | 0.3 | 0.4 |
| Load forecast | 0.4 | 0.23 | 0.48 | 0.2 | 0.1 |
If we want to predict the NRV for time step 5 using the information we have beforehand, we can use the NRV from the previous time steps. We can however also use the load forecast of time step 5. To incorporate this information as input, we need to move the load forecast one time step back. This means, that the input for time step 5 is given with the NRV of time step 4.
If the time is also wanted as input, we add this as a feature for every timestep aswell.
Example summary of the LSTM/GRU Model
==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
Sequential [512, 13] --
├─TimeEmbedding: 1-1 [512, 96, 5] --
│ └─Embedding: 2-1 [512, 96, 4] 384
├─GRUModel: 1-2 [512, 13] --
│ └─GRU: 2-2 [512, 96, 512] 3,949,056
│ └─Linear: 2-3 [512, 13] 6,669
==========================================================================================
Total params: 3,956,109
Trainable params: 3,956,109
Non-trainable params: 0
Total mult-adds (G): 194.11
==========================================================================================
Input size (MB): 0.39
Forward/backward pass size (MB): 202.95
Params size (MB): 15.82
Estimated Total Size (MB): 219.17
==========================================================================================
| Experiment | Quarter | Load forecast | test_L1Loss | test_CRPSLoss |
|---|---|---|---|---|
| Link | False | False | 104.91248365620233 | 80.52249167947208 |
| Link | True | False | 104.01024075423138 | 79.42769390928979 |
| Link | True | True | 103.57896084611653 | 79.2824327805463 |
Quantiles Histogram Debug Plots
Results for now
| Model | test_L1Loss | test_CRPSLoss |
|---|---|---|
| Linear Model | 104.82491272720578 | 77.90755403958835 |
| Non Linear Model | 103.89383283348461 | 77.7099763430082 |
| LSTM/GRU Model | 103.57896084611653 | 79.2824327805463 |
Tasks
- Quantiles zelf breder maken na fitten, literatuur bekijken (overconfident voor ondergrens)
- !!! Historgram quantile plot volledige dag
- CRPS en MAE, over 96 wanneer wordt het slecht
- crps loss fixen
- More input parameters
- [ ] Non autoregressive (baseline for linear)
(
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NRV to prices (bid ladder from yesterday) -> Elia
-
Simple policy )
-
Diffusion vs Variable Selection Network