A hybrid intelligent algorithm based short-term load forecasting approach

The Theta method attracted the attention of researchers and practitioners in recent years due to its simplicity and superior forecasting accuracy. Its performance has been confirmed by many empirical studies as well as forecasting competitions. In this article the Theta method is tested in short-term load forecasting problem. The load time series expressing multiple seasonal cycles is decomposed in different ways to simplify the forecasting problem. Four variants of input data definition are considered. The standard Theta method is uses as well as the dynamic optimised Theta model proposed recently. The performances of the Theta models are demonstrated through an empirical application using real power system data and compared with other popular forecasting methods.

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Short Term Load Forecasting Using Neural Network Trained with Genetic Algorithm & Particle Swarm Optimization

2008 First International Conference on Emerging Trends in Engineering and Technology ◽

10.1109/icetet.2008.94 ◽

2008 ◽

Cited By ~ 22

Author(s):

Sanjib Mishra ◽

Sarat Kumar Patra

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Load Forecasting ◽

Particle Swarm ◽

Short Term ◽

Swarm Optimization ◽

Short Term Load Forecasting

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Research on Short-Term Load Forecasting Method of Power System Based on Seq2Seq-Attention Model

2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2) ◽

10.1109/ei250167.2020.9346583 ◽

2020 ◽

Author(s):

Wei Hu ◽

Yi Zhang ◽

Qiuting Guo ◽

Xu Huang ◽

Gang Li ◽

...

Keyword(s):

Power System ◽

Load Forecasting ◽

Short Term ◽

Attention Model ◽

Forecasting Method ◽

Short Term Load Forecasting

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An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

Sensors ◽

10.3390/s21051639 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1639

Author(s):

Seungmin Jung ◽

Jihoon Moon ◽

Sungwoo Park ◽

Eenjun Hwang

Keyword(s):

Power Consumption ◽

Prediction Models ◽

Short Term Memory ◽

Load Forecasting ◽

Input Sequence ◽

Short Term ◽

Performance Improvements ◽

Short Term Load Forecasting ◽

Significant Performance ◽

Input Variables

Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

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