Utilization of Artificial Neural Networks for Precise Electrical Load Prediction
Abstract
:1. Introduction
- Long-term forecasting (LTF): 1–20 years. The LTF is crucial for the inclusion of new-generation units in the system and the development of the transmission system.
- Medium-term forecasting (MTF): 1 week–12 months. The MTF is most helpful for the setting of tariffs, the planning of the system maintenance, financial planning, and the scheduling of fuel supply.
- Short-term forecasting (STF): 1 h–1 week. The STF is necessary for the data supply to the generation units to schedule their start-up and shutdown time, to prepare the spinning reserves, and to conduct an in-depth analysis of the restrictions in the transmission system. STF is also crucial for the evaluation of power system security.
2. Theoretical Background
2.1. RNN for Variable Inputs/Outputs
- One to Many, applied in fields of image captioning, text generation
- Many to One, applied in fields of sentiment analysis, text classification
- Many to Many, applied in fields of machine translation, voice recognition
2.2. Vanilla RNN
- Inputs and outputs are of variable size
- In each stage the hidden state from the previous stage as well as the current input is utilized to compute the current hidden state that feeds the next stage. Consequently, knowledge from past data is transmitted through the hidden states to the next stages. Hence, the hidden state is a means of connecting the past with the present as well as input with output.
- The set of parameters U, V, and W as well as the activation function are common to all RNN cells.
2.3. Long Short-Term Memory
2.4. Convolutional Neural Network
2.5. Gated Recurrent Unit
3. Materials and Methods
3.1. Dataset
3.2. Proposed Methodology
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AGC | Automatic Generation Control |
ANN | Artificial Neural Network |
CNN | Convolutional Neural Network |
DSO | Distribution System Operators |
ELD | Economic Load Dispatch |
GRU | Gated Recurrent Unit |
HETS | Hellenic Electricity Transmission System |
IPTO | Independent Power Transmission Operator |
LD | Linear dichroism |
LSTM | Long Short-Term Memory |
LTF | Long-term forecasting |
MAE | Mean Absolute Error |
MTF | Medium-term forecasting |
RMSE | Root Mean Square Error |
RNN | Recurrent Neural Network |
STF | Short-term forecasting |
TSO | Transmission System Operator |
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Time Horizon | Area of Application |
---|---|
12 months–20 months | Planning of the Power System |
1 week–12 months | Scheduling the maintenance of the power system elements |
1 min–1 week | Commitment analysis of the power units |
Automatic Generation Control (AGC) | |
Economic load dispatch (ELD) | |
ms–s | Power system dynamic analysis |
ns–ms | Power system transient analysis |
Time Step | 12 | 24 | 48 | 72 | |||||
---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | ||
RNN | Training set | 0.032 | 0.161 | 0.038 | 0.171 | 0.051 | 0.210 | 0.032 | 0.163 |
Testing set | 0.048 | 0.165 | 0.033 | 0.163 | 0.041 | 0.187 | 0.070 | 0.244 | |
LSTM | Training set | 0.024 | 0.129 | 0.030 | 0.145 | 0.030 | 0.153 | 0.054 | 0.219 |
Testing set | 0.050 | 0.162 | 0.055 | 0.228 | 0.051 | 0.207 | 0.115 | 0.324 | |
GRU | Training set | 0.026 | 0.137 | 0.032 | 0.143 | 0.029 | 0.148 | 0.031 | 0.141 |
Testing set | 0.057 | 0.188 | 0.033 | 0.165 | 0.040 | 0.182 | 0.040 | 0.186 |
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Pavlatos, C.; Makris, E.; Fotis, G.; Vita, V.; Mladenov, V. Utilization of Artificial Neural Networks for Precise Electrical Load Prediction. Technologies 2023, 11, 70. https://doi.org/10.3390/technologies11030070
Pavlatos C, Makris E, Fotis G, Vita V, Mladenov V. Utilization of Artificial Neural Networks for Precise Electrical Load Prediction. Technologies. 2023; 11(3):70. https://doi.org/10.3390/technologies11030070
Chicago/Turabian StylePavlatos, Christos, Evangelos Makris, Georgios Fotis, Vasiliki Vita, and Valeri Mladenov. 2023. "Utilization of Artificial Neural Networks for Precise Electrical Load Prediction" Technologies 11, no. 3: 70. https://doi.org/10.3390/technologies11030070