Multi-objective LSTM ensemble model for household short-term load forecasting

Fan, Chaodong; Li, Yunfan; Yi, Lingzhi; Qu, Xiaobo; Ai, Zhaoyang

doi:10.1007/s12293-022-00355-y

Cited by 16 publications

(6 citation statements)

References 36 publications

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“…With the rise of deep learning technology, more specialized models, such as RNN and CNN, have been used in STLF. Fan et al [44] used a multi-objective LSTM-integrated model to predict household load, and a combination strategy of DSNs to combine individual predictions to form a set prediction. However, LSTM does not work well in the face of longer sequences and is time-consuming because it cannot be paralleled.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

A hybrid prediction method for short‐term load based on temporal convolutional networks and attentional mechanisms

Tian

Chen

et al. 2023

IET Generation Trans & Dist

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Accurate power load prediction is an important guide for power system planning and operation. High‐ or low‐load prediction results will affect the operation of the power system. In recent years, deep learning technology represented by convolution neural network (CNN) and transformer has been proved to be suitable for power load prediction. This paper proposes a new short‐term power load hybrid forecasting model, called channel enhanced attention (CEA) and temporal convolutional network (TCN)‐based transformer comprehensive forecasting model. This method combines the short‐term feature extraction ability of TCN with the long‐term dependent capture ability of transformer for short‐term load forecasting. And the CEA designed in this study is added to improve the prediction accuracy. On the same dataset, the designed model predicts power load mean square errors of 0.056 and 0.146 for the next 24 h and the next week, respectively, which is 0.002 to 0.073 and 0.012 to 0.024 lower than the baseline model. The experimental results show that the hybrid short‐term power load prediction model proposed in this paper is significantly better than the existing methods. The predicted curve is in agreement with the actual charge change, which provides a good guidance for short‐term power load prediction.

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Section: Deep Learning Methodsmentioning

confidence: 99%

A hybrid prediction method for short‐term load based on temporal convolutional networks and attentional mechanisms

Tian

Chen

et al. 2023

IET Generation Trans & Dist

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“…Multi-objective optimization problems are known to be efficiently handled using Evolutionary Algorithms (EAs) [41,42]. EAs are crafted to generate and update a diverse population of solutions.…”

Section: One-pass Neuroevolutionary Multitaskingmentioning

confidence: 99%

Jack and Masters of All Trades: One-Pass Learning of a Set of Model Sets from Foundation Models

Choong¹,

Ong²,

Gupta³

et al. 2022

Preprint

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For deep learning, size is power. Massive neural nets trained on broad data for a spectrum of tasks are at the forefront of artificial intelligence. These foundation models or 'Jacks of All Trades' (JATs), when fine-tuned for downstream tasks, are gaining importance in driving deep learning advancements. However, environments with tight resource constraints, changing objectives and intentions, or varied task requirements, could limit the real-world utility of a singular JAT. Hence, in tandem with current trends towards building increasingly large JATs, this paper conducts an initial exploration into concepts underlying the creation of a diverse set of compact machine learning model sets. Composed of many smaller and specialized models, we formulate the Set of Sets to simultaneously fulfil many task settings and environmental conditions. A means to arrive at such a set tractably in one pass of a neuroevolutionary multitasking algorithm is presented for the first time, bringing us closer to models that are collectively 'Masters of All Trades'.

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“…This paper presents a comparative analysis implementing state-of-the-art machine learning and deep learning methods on MTLF at AL. A great number of robust and most-practiced ELF models as of the date are performed: LR [9][10][11][12], DT [13], RF [14][15][16], gradient boosting [17,18], AdaBoost [19][20][21][22] as the representatives of ML methods; RNN [23] and LSTM [24][25][26][27][28][29] as the representatives of DL models. The ELF results by all these methods have been achieved as daily forecasting steps for monthly forecasting intervals.…”

Section: Article Info Abstractmentioning

confidence: 99%

Comparison of robust machine-learning and deep-learning models for midterm electrical load forecasting

Yaprakdal

Bal

2022

EJT

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Electrical load forecasting (ELF) is gaining importance especially due to the severe impact of climate change on electrical energy usage and dynamically evolving smart grid technologies in the last decades. In this regard, medium-term load forecasting, a crucial need for power system planning (generation optimization and outages plan) and operation control, has become prominent in particular. Machine learning and deep learning-based techniques are currently trending approaches in electrical load estimation due to their capability to model complex non-linearity, feature abstraction and high accuracy, especially in the smart power systems environment. In this study, several load forecasting models based on machine learning methods which comprise linear regression (LR), decision tree (DT), random forest (RF), gradient boosting, adaBoost, and deep learning techniques such as recurrent neural network (RNN) and long short-term memory (LSTM) are studied for medium-term electrical load demand forecasting at an aggregated level. Performance metric results of these analyzes are presented in detail. State-of-the-art feature selection models are examined on the dataset and their effects on these forecasting methods are evaluated. Numerical results show that forecasting performance can be significantly improved. These results are validated by the results of other studies on the subject and found to be superior.

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Multi-objective LSTM ensemble model for household short-term load forecasting

Cited by 16 publications

References 36 publications

A hybrid prediction method for short‐term load based on temporal convolutional networks and attentional mechanisms

A hybrid prediction method for short‐term load based on temporal convolutional networks and attentional mechanisms

Jack and Masters of All Trades: One-Pass Learning of a Set of Model Sets from Foundation Models

Comparison of robust machine-learning and deep-learning models for midterm electrical load forecasting

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