Transfer Learning by Similarity Centred Architecture Evolution for Multiple Residential Load Forecasting

The development from traditional low voltage grids to smart systems has become extensive and adopted worldwide. Expanding the demand response program to cover the residential sector raises a wide range of challenges. Short term load forecasting for residential consumers in a neighbourhood could lead to a better understanding of low voltage consumption behaviour. Nevertheless, users with similar characteristics can present diversity in consumption patterns. Consequently, transfer learning methods have become a useful tool to tackle differences among residential time series. This paper proposes a method combining evolutionary algorithms for neural architecture search with transfer learning to perform short term load forecasting in a neighbourhood with multiple household load consumption. The approach centres its efforts on neural architecture search using evolutionary algorithms. The neural architecture evolution process retains the patterns of the centre-most house, and later the architecture weights are adjusted for each house in a multihouse set from a neighbourhood. In addition, a sensitivity analysis was conducted to ensure model performance. Experimental results on a large dataset containing hourly load consumption for ten houses in London, Ontario showed that the performance of the proposed approach performs better than the compared techniques. Moreover, the proposed method presents the average accuracy performance of 3.17 points higher than the state-of-the-art LSTM one shot method.

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Two-Layer Transfer-Learning-Based Architecture for Short-Term Load Forecasting

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2019.2924326 ◽

2020 ◽

Vol 16 (3) ◽

pp. 1722-1732 ◽

Cited By ~ 1

Author(s):

Long Cai ◽

Jie Gu ◽

Zhijian Jin

Keyword(s):

Transfer Learning ◽

Load Forecasting ◽

Layer Transfer ◽

Short Term ◽

Short Term Load Forecasting

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Short-Term Load Forecasting Method for Low Voltage Users Based on Deep Belief Neural Network

2020 IEEE Sustainable Power and Energy Conference (iSPEC) ◽

10.1109/ispec50848.2020.9351039 ◽

2020 ◽

Author(s):

Qing Cheng ◽

Xubin Pei ◽

Zhou Fang ◽

Zhengbo Chen ◽

Jie Yin ◽

...

Keyword(s):

Neural Network ◽

Low Voltage ◽

Load Forecasting ◽

Short Term ◽

Forecasting Method ◽

Short Term Load Forecasting

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Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting

Energies ◽

10.3390/en14217378 ◽

2021 ◽

Vol 14 (21) ◽

pp. 7378

Author(s):

Pedro M. R. Bento ◽

Jose A. N. Pombo ◽

Maria R. A. Calado ◽

Silvio J. P. S. Mariano

Keyword(s):

Deep Learning ◽

New England ◽

Network Architecture ◽

Prediction Models ◽

Load Forecasting ◽

Training Data ◽

Constant Field ◽

Short Term ◽

Wide Range ◽

Short Term Load Forecasting

Short-Term Load Forecasting is critical for reliable power system operation, and the search for enhanced methodologies has been a constant field of investigation, particularly in an increasingly competitive environment where the market operator and its participants need to better inform their decisions. Hence, it is important to continue advancing in terms of forecasting accuracy and consistency. This paper presents a new deep learning-based ensemble methodology for 24 h ahead load forecasting, where an automatic framework is proposed to select the best Box-Jenkins models (ARIMA Forecasters), from a wide-range of combinations. The method is distinct in its parameters but more importantly in considering different batches of historical (training) data, thus benefiting from prediction models focused on recent and longer load trends. Afterwards, these accurate predictions, mainly the linear components of the load time-series, are fed to the ensemble Deep Forward Neural Network. This flexible type of network architecture not only functions as a combiner but also receives additional historical and auxiliary data to further its generalization capabilities. Numerical testing using New England market data validated the proposed ensemble approach with diverse base forecasters, achieving promising results in comparison with other state-of-the-art methods.

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Short-Term Load Forecasting Using Neural Networks with Pattern Similarity-Based Error Weights

Energies ◽

10.3390/en14113224 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3224

Author(s):

Grzegorz Dudek

Keyword(s):

Time Series ◽

Test Pattern ◽

Empirical Studies ◽

Load Forecasting ◽

Learning Performance ◽

Short Term ◽

Training Samples ◽

Wide Range ◽

Pattern Similarity ◽

Short Term Load Forecasting

Forecasting time series with multiple seasonal cycles such as short-term load forecasting is a challenging problem due to the complicated relationship between input and output data. In this work, we use a pattern representation of the time series to simplify this relationship. A neural network trained on patterns is an easier task to solve. Thus, its architecture does not have to be either complex and deep or equipped with mechanisms to deal with various time-series components. To improve the learning performance, we propose weighting individual errors of training samples in the loss function. The error weights correspond to the similarity between the training pattern and the test query pattern. This approach makes the learning process more sensitive to the neighborhood of the test pattern. This means that more distant patterns have less impact on the learned function around the test pattern and lead to improved forecasting accuracy. The proposed framework is useful for a wide range of complex time-series forecasting problems. Its performance is illustrated in several short-term load-forecasting empirical studies in this work. In most cases, error weighting leads to a significant improvement in accuracy.

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