This document evaluates different deep learning algorithms and data preprocessing techniques for demand power prediction. It finds that a recurrent neural network model achieves the best prediction performance. All algorithms show improved accuracy when trained on preprocessed data that balances the dimension of power load and weather feature data, rather than raw data of varying dimensions. Further research into prediction using extreme learning machine algorithms is suggested.

1. Evaluation of demand power prediction
performance based on deep learning
algorithm and data preprocessing
Park Jee Hyun
28 APR 2017
-
SungKyunGwan University
Nanyang Technological University

2. Contents
• Purpose
• Experiments
• Training & Testing Algorithm
• Raw vs. Preprocessed
• Results
• Single Layer Perceptron Network
• Multiple Layer Perceptron Network
• Convolutional Neural Network
• Recurrent Neural Network
• Conclusions
• Further Studies

3. Purpose
• Goal: Achieving high load forecasting accuracy.
• Strategies for forecasting more accurate demand load.
• Model structure aspect
• Select the proper algorithm structure for the demand load forecasting.
• Data aspect
• Increases the amount of data features.
• Improves the quality of data.
• The purpose of this paper:
• Model structure aspect
• We will investigate which deep learning model structure is most suitable for forecasting demand load.
• Data aspect
• For more data features, we will use weather data as well as demand load data.
• However, in using weather data, there is a drawback that this data is very low in data dimension compared to demand
load data. If the model is trained using data of different dimensions, there is a problem that the features are not learned
correctly.
 We devised a proper data preprocessing which improves the quality of the data and allows better performance.

4. Experiments
Data aspect
Model structure
aspect

5. Training & Testing Algorithm
Input
Data
Target
Data
Training
Model
Input
Data
Trained
Model
Predicted
Data
a) Training
b) Testing
transplant
trained
model
feature data predicting
feature data labeled data

6. Raw vs. Preprocessed
• Given data(Australia NEM data):
• Daily demand load data with 30 mins interval. ( 48 points/day)
• Daily Max and Mean Temperature data. ( 2 points/day)
• Raw data:
• [𝑃𝑟𝑎𝑤
𝑛
] = 𝑃1
𝑛
, … , 𝑃48
𝑛
, 𝑇 𝑚𝑎𝑥
𝑛
, 𝑇 𝑚𝑒𝑎𝑛
𝑛
( 50 points/day)
• [𝑃1
𝑛
, … , 𝑃48
𝑛
]: demand load data of day n (48 points/day)
• 𝑇 𝑚𝑎𝑥
𝑛
∶ max temperature data of day n (1point/day)
• 𝑇 𝑚𝑒𝑎𝑛
𝑛
∶ mean temperature data of day n (1point/day)
 Problem address:
• Dimension of temperature data & power load data are unbalanced.
• High-dimensional data overwhelms low-dimensional data, preventing accurate features from being learned during model training.

7. Raw vs. Preprocessed
 Proposed approach:
• In order to prevent low-dimensional data from being ignored during model training process,
add proper data preprocessing process.
• It is expected that accuracy and stability of prediction will be improved by appropriately reflecting two
types of data with different dimensions in the model training process.
• Preprocessed data:
• [𝑃𝑝𝑟𝑒
𝑛
] = 𝑃1
𝑛
, … , 𝑃48
𝑛
, 𝑃𝑇.𝑚𝑎𝑥_𝑛𝑜𝑟𝑚
𝑛
, 𝑃 𝑇.𝑚𝑒𝑎𝑛_𝑛𝑜𝑟𝑚
𝑛
(144 points/day)
• [𝑃1
𝑛
, … , 𝑃48
𝑛
] : demand load data of day n (48 points/day)
• [𝑃 𝑇.max_𝑛𝑜𝑟𝑚
𝑛
] = [ 𝑃1
𝑛 ^𝑇 𝑚𝑎𝑥_𝑛𝑜𝑟𝑚
𝑛
, … , 𝑃48
𝑛 ^𝑇 𝑚𝑎𝑥_𝑛𝑜𝑟𝑚
𝑛
] (48 points/day)
• [𝑃 𝑇.𝑚𝑒𝑎𝑛_𝑛𝑜𝑟𝑚
𝑛
] = [ 𝑃1
𝑛 ^𝑇 𝑚𝑒𝑎𝑛_𝑛𝑜𝑟𝑚
𝑛
, … , 𝑃48
𝑛 ^𝑇 𝑚𝑒𝑎𝑛_𝑛𝑜𝑟𝑚
𝑛
] (48 points/day)

8. Results
• RMSE & standard deviation score
Raw [RMSE / std] Preprocessed [RMSE / std]
Single Layer Perceptrons 6.30388 / 1.0803 1.86425 / 0.314612
Multiple Layer Perceptrons 27.0591 / 23.0247 2.2562 / 3.21933
Convolutional Neural Network 4.61754 / 3.52584 2.63669 / 1.11987
Recurrent Neural Network 0.0964969 / 0.00310329 0.0696449 / 0.00145637

9. Single Layer Perceptron

10. Multiple Layer Perceptron

11. Convolutional Neural Network

12. Recurrent Neural Network

13. Conclusions
• Model structure aspect
• RNN model shows the best performance among four learning algorithm designed.
 The results confirm that the RNN model is most suitable for
the power demand forecasting.
• Data aspect
• All algorithms show better and stable RMSE score with preprocessed data.
 We have shown that applying a proper data preprocessing to
different dimensional data allows the model to improve its
performance.

14. Further Studies
• All neural networks in this paper are based on gradient descent algorithm.
• Extreme learning machine(ELM) is a learning algorithm based on single hidden layer feed-forward neural
network (SLFN).
• Hierarchical ELM(H-ELM) is a framework of multilayer implementation of the ELM, which improves the
learning performance of the original ELM, while maintaining its advantages of training efficiency.
 Need further study about power load predicting algorithm based on ELM and H-ELM.
 Check ELM and H-ELM can outperform gradient based machine and find out the reasons if it outperform.