Typical load profile-supported convolutional neural network for short-term load forecasting in the industrial sector

Walser, Thilo; Sauer, Alexander

doi:10.1016/j.egyai.2021.100104

Cited by 40 publications

(8 citation statements)

References 18 publications

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“…An artificial neural network (ANN) is an automatic learning and processing paradigm inspired by the functioning of the human nervous system [58], [59], [65]- [67], [74]. A neural network is composed of a set of neurons interconnected by links, where each neuron takes as inputs the outputs of the preceding neurons, multiplies each of these inputs by a weight and, by means of an activation function, calculates an output.…”

Section: Methodsmentioning

confidence: 99%

“…The union of all these interconnected neurons the artificial neural network [50], [51], [54], [55]. The artificial neural network as well as biological networks learn by repetition, and the more data you must train and the more times you train the network the better results you will get [62], [63], [67]. Training an ANN is a process that modifies the value of the weights associated with each neuron, so that the ANN can generate an output from the data presented in the input [52].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Alarcón¹,

Rivera-M²,

Golondrino³

2022

IJACSA

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The concentrate process is the most sensitive in mineral processing plants (MPP), and the optimization of the process based on intelligent computational models (machine learning for recovery percentage modelling) can offer significant savings for the plant. Recent theoretical developments have revealed that many of the parameters commonly assumed as constants in gravity concentration modelling have a dynamic nature; however, there still lacks a universal way to model these factors accurately. This paper aims to understand the model effect of operational parameters of a jig (gravimetric concentrator) on the recovery percentage of the interest mineral (gold) through empirical modeling. The recovery percentage of mineral particles in a vibrated bed of big particles is studied by experimental data. The data used for the modelling were from experimental test in a pilot-scale jig supplemented by a twomonth field sampling campaign for collecting 151 tests varying the most significant parameters (amplitude and frequency of pulsation, water flow, height of the artificial porous bed, and particle size). It is found the recovery percentage (%R) decreases with increasing pulsation amplitude (A) and frequency (F) when the size ratio of small to large particles (d/D) is smaller than 0.148. An empirical model was developed through machine learning techniques, specifically an artificial neural network (ANN) model was built and trained to predict the jig recovery percentage as a function of operation parameters and is then used to validate the recovery as a function of vibration conditions. The performance of the ANN model was compared with a new 65 experimental data of the recovery percentage.Results showed that the model (R 2 = 0.9172 and RMSE = 0.105) was accurate and therefore could be efficiently applied to predict the recovery percentage in a jig device.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Alarcón¹,

Rivera-M²,

Golondrino³

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Existing time-series prediction approaches rely on automatic feature selection by machine learning models to identify relevant variables, while supporting safe situation prediction based on multiple time series and measurements [32]. Most of the existing research articles apply a combination of multiple model architectures, and each combined model will also have a specific domain of applicability as well as a corresponding accuracy.…”

Section: Related Workmentioning

confidence: 99%

Long-Term Prediction of Network Security Situation Through the Use of the Transformer-Based Model

Yin¹,

Yang²,

Yao³

et al. 2022

IEEE Access

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In recent years, the Internet has shown rapid development, and network security issue has gradually become the focus of research by scholars and enterprises. Network security time series is a reliable source to obtain future network security situation, so as to develop network security defense strategy by exploring the correlation of time series. The network security time series is a reliable source to obtain the future network security situation, and it is the main direction of current network security defense by exploring the correlation of time series, and analyzing the future network security situation so as to formulate network security defense strategies. This is the main direction of network security defense. The existing research focuses on the short-term prediction of network attacks, and the robustness and accuracy of long-term prediction still have big problems. To fuse the information from different data sources and capture the correlation between sequences, we design a data source selection module based on the similarity of measurement curves. We then model the network security situation prediction based on deep learning models and propose a situation prediction model based on Temporal Convolutional Network (TCN)combined Transformer, which focuses on the time series long-term prediction problem, combining the network condition and attack situation to obtain the future network security situation. Our proposed model is divided into three parts, which are the information encoding module, the information synthesis module, and situation value calculation and prediction accuracy evaluation module. The selected multi-dimensional situations element data are used as model input, and the TCN-combined Transformer is employed as the network security situational data processing unit to complete the information fusion and prediction tasks. Finally, the role of data source selection on prediction accuracy is evaluated using an ablation study. We experimented and evaluated the model at different prediction horizon lengths using five existing baseline models and three performance metrics. The experimental results show that our proposed prediction model has better robustness and accuracy in most of the metrics.

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“…Diese Prognosen können wiederum für eine effektivere Planung des Energieverbrauchs und die Umsetzung der oben genannten Ziele genutzt werden [6]. Sowohl in der wissenschaftlichen Literatur als auch in der Praxis werden meistens die Verbräuche am Hauptzähler auf Werksebene genutzt, da dies dem Standard in der Kommunikation mit dem Energieversorger entspricht [7].…”

Section: Introductionunclassified

Bottom-Up Lastprognose im industriellen Kontext/Bottom-up load forecasting in the industrial context

Baur¹,

Kaymakci²,

Sauer³

2023

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In Zeiten volatiler und steigender Energiepreise gewinnt die Lastprognose als Entscheidungsgrundlage zunehmend an Bedeutung. Die fortschreitende Digitalisierung erlaubt zudem ein detailliertes Aufzeichnen von Verbrauchsdaten auf mehreren Abstraktionsebenen. In diesem Beitrag werden zwei Ansätze, die eine Messzählertopologie in die Prognose integrieren, mit dem Standardansatz auf Hauptzählerebene verglichen. Die Ergebnisse auf den Unternehmensdaten motivieren für einen flächendeckenden Einsatz. Increasingly volatile and rising energy prices make load forecasting increasingly important as a basis for decision-making. Moreover, the advancing digitalization allows a detailed recording of consumption data on several abstraction levels. This paper compares two approaches that integrate a metering topology into load forecasting with the standard approach at the main meter level. The results on enterprise data motivate widespread deployment.

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Typical load profile-supported convolutional neural network for short-term load forecasting in the industrial sector

Cited by 40 publications

References 18 publications

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Long-Term Prediction of Network Security Situation Through the Use of the Transformer-Based Model

Bottom-Up Lastprognose im industriellen Kontext/Bottom-up load forecasting in the industrial context

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