Comparative Analysis of Multi-Step Time-Series Forecasting for Network Load Dataset

Sahoo, Debashis; Sood, Naveksha; Rani, Usha; Abraham, George; Dutt, Varun; Dileep, A. D.

doi:10.1109/icccnt49239.2020.9225449

Cited by 18 publications

(10 citation statements)

References 22 publications

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“…Then during training, the system weights are adjusted by minimizing this loss function at every r-step (0 ≤ r ≤ L) for every t-step. In another word, RGD allows the system weights updated from minimizing the accumulated error at smaller r-step than t-step, as presented in ( 27) and (28), which demonstrates the strength of RGD at learning accumulated prediction errors.…”

Section: Recursive Gradient Descentmentioning

confidence: 90%

“…Their method requires data distribution with strong and clear seasonality variations, where industrial process data usually do not have. The studies in [28] and [29] compare different time-series prediction strategies for a single LSTM unit prediction whereas [30] proposes a stacked LSTM network using multiple units to maintain higher prediction capacity. However, the scope of those studies are simply on reducing autoregression errors rather than making precise fault prediction.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Fault Prediction in Wireless Sensor Network Embedded Cyber-Physical Systems for Industrial Processes

et al. 2022

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This paper investigates the challenging fault prediction problem in process industries that adopt autonomous and intelligent cyber-physical systems (CPS), which is in line with the emerging developments of industrial internet of things (IIoT) and Industry 4.0. Particularly, we developed an end-to-end deep learning approach based on a large volume of real-time sensory data collected from a chemical plant equipped with wireless sensors. Firstly, a novel recursive architecture with multi-lookback inputs is proposed to perform autoregression on imbalanced time-series data as a preliminary prediction. In this process, a novel learning algorithm named recursive gradient descent (RGD) is developed for the proposed architecture to reduce cumulative prediction uncertainties. Subsequently, a classification model based on temporal convolutions over multiple channels with decay effect is proposed to perform multi-class classification for fault root cause identification and localization. The overall network is named the cumulative uncertainty reduction network (CURNet), for its superior capacity in reducing prediction uncertainties accumulated over multiple prediction steps. Performance evaluations show that CURNet is able to achieve superior performance especially in terms of fault prediction recall and fault type classification accuracy, compared to the existing techniques.

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Section: Recursive Gradient Descentmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Fault Prediction in Wireless Sensor Network Embedded Cyber-Physical Systems for Industrial Processes

et al. 2022

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“…MIMO strategy presents a model that aims to predict a large number of values at once, which defines a time sequence of the predicted values. This approach is well illustrated in other articles such as [21], [22], and [23]. Therefore, the grid search technique is also used to optimize the hyperparameters of the model.…”

Section: Literature Reviewmentioning

confidence: 97%

“…Moreover, the data set used is composed of samples at a 15-minute interval, collected over a month, in comparison with our dataset that is composed of samples at a 1-hour interval, collected over 5 years. In this work, a model performs well if the RMSE value is as small as possible, this approach being frequently found in the literature [21], [22], [23], [24], [25], [26].…”

Section: Literature Reviewmentioning

confidence: 98%

“…Therefore, the grid search technique is also used to optimize the hyperparameters of the model. This optimization method for the MIMO strategy is also used in [22] to determine the number of neurons in each layer, the number of samples used as input, the batch size, and the number of epochs. Moreover, the data set used is composed of samples at a 15-minute interval, collected over a month, in comparison with our dataset that is composed of samples at a 1-hour interval, collected over 5 years.…”

Section: Literature Reviewmentioning

confidence: 99%

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Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks

et al. 2023

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Photovoltaic energy production is an important factor for increasing the electricity supply. The ability to predict the electric power production (EPP) of a photovoltaic (PV) farm supports from the management process of the power grid to the trade in the energy market and much more. Also, by predicting the production of PV power (PVP), it is possible to monitor the lifetime of the solar cells that form the backbone of any solar PV system. As a critical result, sudden failures of the PV plant can be avoided. Using a long short-term memory recurrent neural network (LSTM-RNN) model, this work evaluates the prediction accuracy of two forecasting strategies: the recursive strategy and the non-recursive Multiple-Input and Multiple-Output, respectively. The dataset consists of 5-years in-filed production data measurements collected from the CETATEA photovoltaic power plant, a research site facility for renewable energies located in Cluj-Napoca, Romania. The high granularity of the electric power production dataset values recorded each 1 hour guarantees the overall prediction accuracy of the system. The impact of the dataset size, the number of previous observations, and the forecast horizon on the neural network prediction accuracy is evaluated for each strategy. The performance metrics used to evaluate the prediction accuracy are the root mean square error, the mean bias error, and the mean average error. The results analysis demonstrates the ability of the implemented machine learning models to predict electric power production, as well as their importance in the energy loss management process.

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The dependence of forecasts on sampling frequency as a guide to optimizing monitoring in community ecology

Daugaard,

Merkli,

Merz

et al. 2024

Ecosphere

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Facing climate change and biodiversity loss, it is critical that ecology advances so that processes, such as species interactions and dynamics, can be correctly estimated and skillfully forecasted. As different processes occur on different time scales, the sampling frequency used to record them should intuitively match these scales. Yet, the effect of data sampling frequency on ecological forecasting accuracy is understudied. Using a simple simulated dataset as a baseline and a more complex high‐frequency plankton dataset, we tested how different sampling frequencies impacted abundance forecasts of different plankton classes and the estimation of their interactions. We then investigated whether plankton growth rates and body sizes could be used to select the most appropriate sampling frequency. The simple simulated dataset showed that the optimal sampling frequency scaled positively with growth rate. This finding was not repeated in the analyses of the plankton time series, however. There, we found that a reduction in sampling frequency worsened forecasts and led us to both over‐ and underestimate plankton interactions. This suggests that forecasting can be used to determine the ideal sampling frequency in scientific and monitoring programs. A better study design will improve theoretical understanding of ecology and advance policy measures dealing with current global challenges.

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Comparative Analysis of Multi-Step Time-Series Forecasting for Network Load Dataset

Cited by 18 publications

References 22 publications

Deep Learning-Based Fault Prediction in Wireless Sensor Network Embedded Cyber-Physical Systems for Industrial Processes

Deep Learning-Based Fault Prediction in Wireless Sensor Network Embedded Cyber-Physical Systems for Industrial Processes

Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks

The dependence of forecasts on sampling frequency as a guide to optimizing monitoring in community ecology

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