Ensemble learning models for short-term electricity demand forecasting

Ghareeb, Ahmed; Al-bayaty, Hussein; Haseeb, Qubad Sabah; Zeinalabideen, Mohammed Jawad

doi:10.1109/icdabi51230.2020.9325623

Cited by 6 publications

(1 citation statement)

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“…The authors showed that the proposed method outperformed other methods, especially when the amount of past training sampled was small. In [21], the authors showed that averaging the short-term electricity forecasting results of three models: generalized linear model, ANNs and RFs resulted in improvement compared to individual forecasting performance.…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of single and ensemble CNN, LSTM and traditional ANN models for short‐term electricity load forecasting

Kathirgamanathan

Patel

Khwaja

et al. 2022

The Journal of Engineering

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The authors propose bagged and boosted convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, and compare their performance with the bagged and boosted traditional shallow artificial neural networks (ANNs) for short-term electricity load forecasting. Unlike existing references that mainly compare the performance of ensemble deep learning with single deep learning and machine learning techniques, three further performance comparisons are carried out: (1) bagged CNNs and bagged LSTMs, (2) boosted CNNs and LSTMs, and (3) bagged CNNs and bagged LSTMs, and boosted CNNs and LSTMs. This allows an insight into the individual effects of ensemble learning on CNNs and LSTMs. The proposed models' inputs consist of weather and time-related features in addition to the past load. The use of these features allows CNNs and LSTMs to estimate further complex relationship between them and the load. We implement all these methods and compare their performance on the same New England electricity load forecasting data set via statistical analysis. Effects on the forecasting performance with reduced training data are further shown. The LSTM models have the largest performance variation and are also more sensitive to a reduction in training data. In these models, boosting can improve both prediction accuracy and consistency.

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Section: Introductionmentioning

confidence: 99%