Short-Term Electricity Consumption Forecasting Based on the Attentive Encoder-Decoder Model

Song, Wen; Chandramitasari, Widyaning; Weng, Wei; Fujimura, Shigeru

doi:10.1541/ieejeiss.140.846

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…For air quality monitoring data, this model outperforms existing baseline models in prediction accuracy [175]. Another paper proposes an attentive encoder-decoder model with non-linear multilayer correctors for short-term electricity consumption forecasting [176]. Another study introduced an asymmetric encoder-decoder framework using CNN and a regression-based NN to capture spatial and temporal dependencies in energy consumption data from 10 university buildings in China.…”

Section: Applications Of Anns In Manufacturing Systemsmentioning

confidence: 99%

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

Fatima,

Rahimi

2024

Machines

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Time-series forecasting is crucial in the efficient operation and decision-making processes of various industrial systems. Accurately predicting future trends is essential for optimizing resources, production scheduling, and overall system performance. This comprehensive review examines time-series forecasting models and their applications across diverse industries. We discuss the fundamental principles, strengths, and weaknesses of traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES), which are widely used due to their simplicity and interpretability. However, these models often struggle with the complex, non-linear, and high-dimensional data commonly found in industrial systems. To address these challenges, we explore Machine Learning techniques, including Support Vector Machine (SVM) and Artificial Neural Network (ANN). These models offer more flexibility and adaptability, often outperforming traditional statistical methods. Furthermore, we investigate the potential of hybrid models, which combine the strengths of different methods to achieve improved prediction performance. These hybrid models result in more accurate and robust forecasts. Finally, we discuss the potential of newly developed generative models such as Generative Adversarial Network (GAN) for time-series forecasting. This review emphasizes the importance of carefully selecting the appropriate model based on specific industry requirements, data characteristics, and forecasting objectives.

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Section: Applications Of Anns In Manufacturing Systemsmentioning

confidence: 99%

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

Fatima,

Rahimi

2024

Machines

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“…This is the motivation behind why numerous analyses show that the performance of this model reduces as the size of the sequence increments. The attention mechanism 33 is a primary outskirt of deep learning and an advancement of the encoder‐decoder model, created to evade the forgetting of the previous long sequence data. Inspired by previous works, the traffic prediction is defined as a multivariate time series forecasting issue.…”

Section: Related Workmentioning

confidence: 99%

Traffic prediction using MSSBiLS with self‐attention model

Suvitha

Vijayalakshmi

2022

Concurrency and Computation

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Due to gathering and examining a tremendous amount of traffic stream data, traffic blockage is the most important issue in the intelligent transportation framework. The main aim of this research work is "to find the appropriate deep learning time series models, to predict the future traffic of Denmark Aarhus city." In this article, multivariate sequential stacked bidirectional LSTM with self-attention model (MSSBiLS-SA) is proposed to prevent the long term dependencies and also to reduce the error rate.Based on the traffic state, the high, medium, and low range of traffic is predicted to give the best route to the users, and visualization is performed with the help of the Android application. The proposed MSSBiLS-SA attains higher accuracy 94.56% and 95.36%, sensitivity 88.74% and 92.5%, specificity 93.5% and 94.5%, precision 94.23% and 95.5%, F-score 94.5% and 94.5%, and lower time complexity 10.19% and 12.43% shows better performance when compared with the existing methods, such as prediction and classification of traffic location analysis using optimized graph convolutional recurrent neural network and prediction and classification of traffic location analysis based multilayer perceptron network using long short term memory. Finally, the proposed method predicts and classifies the traffic location very accurately.

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“…Applying deep learning (DL) models to multivariate time series data [1] has recently gained growing popularity in a variety of critical application domains such as climate, environment, healthcare [2], finance, as well as other social good domains [3] or Internet of Things driven critical infrastructures [4]. However, the adaptation of deep learning methodology within such safety-critical application scenarios and systems cannot rely only on prediction performance but has to provide human understandable, interpretable, and robust explanations for their decisions.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Attention-Based Interpretability of Deep Learning in Multivariate Time Series Predictions

Barić

Fumić

Horvatić

et al. 2021

Entropy

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The adaptation of deep learning models within safety-critical systems cannot rely only on good prediction performance but needs to provide interpretable and robust explanations for their decisions. When modeling complex sequences, attention mechanisms are regarded as the established approach to support deep neural networks with intrinsic interpretability. This paper focuses on the emerging trend of specifically designing diagnostic datasets for understanding the inner workings of attention mechanism based deep learning models for multivariate forecasting tasks. We design a novel benchmark of synthetically designed datasets with the transparent underlying generating process of multiple time series interactions with increasing complexity. The benchmark enables empirical evaluation of the performance of attention based deep neural networks in three different aspects: (i) prediction performance score, (ii) interpretability correctness, (iii) sensitivity analysis. Our analysis shows that although most models have satisfying and stable prediction performance results, they often fail to give correct interpretability. The only model with both a satisfying performance score and correct interpretability is IMV-LSTM, capturing both autocorrelations and crosscorrelations between multiple time series. Interestingly, while evaluating IMV-LSTM on simulated data from statistical and mechanistic models, the correctness of interpretability increases with more complex datasets.

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Short-Term Electricity Consumption Forecasting Based on the Attentive Encoder-Decoder Model

Cited by 3 publications

References 11 publications

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

Traffic prediction using MSSBiLS with self‐attention model

Benchmarking Attention-Based Interpretability of Deep Learning in Multivariate Time Series Predictions

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