Holidays short-term load forecasting using fuzzy improved similar day method

2012 ◽  
Vol 23 (8) ◽  
pp. 1254-1271 ◽  
Author(s):  
Akbar Ebrahimi ◽  
Amir Moshari
Keyword(s):  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Similar Day

scholarly journals Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2640 ◽  
Author(s):  
Rae-Jun Park ◽  
Kyung-Bin Song ◽  
Bo-Sung Kwon
Keyword(s):  
Reinforcement Learning ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Load Forecasting ◽  
Selection Method ◽  
Selection Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Similar Day ◽  
Reinforcement Learning Algorithm

Short-term load forecasting (STLF) is very important for planning and operating power systems and markets. Various algorithms have been developed for STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based on reinforcement learning is proposed to substitute the dependence on an expert’s experience. The proposed algorithm consists of the selection of similar days, which is based on the reinforcement algorithm, and the STLF, which is based on an artificial neural network. The proposed similar day selection model based on the reinforcement learning algorithm is developed based on the Deep Q-Network technique, which is a value-based reinforcement learning algorithm. The proposed similar day selection model and load forecasting model are tested using the measured load and meteorological data for Korea. The proposed algorithm shows an improvement accuracy of load forecasting over previous algorithms. The proposed STLF algorithm is expected to improve the predictive accuracy of STLF because it can be applied in a complementary manner along with other load forecasting algorithms.

Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks

2010 ◽  
Vol 25 (1) ◽  
pp. 322-330 ◽  
Author(s):  
Ying Chen ◽  
P.B. Luh ◽  
Che Guan ◽  
Yige Zhao ◽  
L.D. Michel ◽  
...  
Keyword(s):  
Neural Networks ◽  
Load Forecasting ◽  
Wavelet Neural Networks ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Similar Day

scholarly journals Short-Term Load Forecasting Based on the Transformer Model

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 516
Author(s):  
Zezheng Zhao ◽  
Chunqiu Xia ◽  
Lian Chi ◽  
Xiaomin Chang ◽  
Wei Li ◽  
...  
Keyword(s):  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Proposed Model ◽  
Consumption Data ◽  
Short Term Load Forecasting ◽  
Distribution Process ◽  
Set Up ◽  
Transformer Model ◽  
Similar Day

From the perspective of energy providers, accurate short-term load forecasting plays a significant role in the energy generation plan, efficient energy distribution process and electricity price strategy optimisation. However, it is hard to achieve a satisfactory result because the historical data is irregular, non-smooth, non-linear and noisy. To handle these challenges, in this work, we introduce a novel model based on the Transformer network to provide an accurate day-ahead load forecasting service. Our model contains a similar day selection approach involving the LightGBM and k-means algorithms. Compared to the traditional RNN-based model, our proposed model can avoid falling into the local minimum and outperforming the global search. To evaluate the performance of our proposed model, we set up a series of simulation experiments based on the energy consumption data in Australia. The performance of our model has an average MAPE (mean absolute percentage error) of 1.13, where RNN is 4.18, and LSTM is 1.93.

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