Machine learning based very short term load forecasting of machine tools

Dietrich, Bastian; Walther, Jessica; Weigold, Matthias; Abele, Eberhard

doi:10.1016/j.apenergy.2020.115440

Cited by 62 publications

(19 citation statements)

References 29 publications

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“…The second most crucial distinction among the research field is the forecasting horizon. Varying from very short-term applications, like forecasting the next 900 s for machine tools [14], moving to a few hours [15], predicting the day-ahead, which is the most common [16], and 48 h ahead [17], to weekly forecasts [18]. The forecasting granularity also varies among the research field.…”

Section: Literature Reviewmentioning

confidence: 99%

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Short-Term Electricity Load Forecasting with Machine Learning

Madrid

António

2021

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An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydrothermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This research proposes a set of machine learning (ML) models to improve the accuracy of 168 h forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model’s interpretation, which provided a relevant additional result, the features’ importance in the forecasting.

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Section: Literature Reviewmentioning

confidence: 99%

“…A grid search was then conducted to explore a finer hyperparameter space based on the randomized search results. This two-step approach [14] attempts to find a global optimum without digging through all the hyperparameter grids in the first step, reducing computational time, and refining the result by searching the second step.…”

Section: Modelingmentioning

confidence: 99%

Short-Term Electricity Load Forecasting with Machine Learning

Madrid

António

2021

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“…Bastian Dietrich [10] developed a Machine learning based very short-term load forecasting of machine tools. The model used a blueprint to develop load forecasting models for machines in order to gain productions using the historic load profile and various machine for data processing.…”

Section: Literature Surveymentioning

confidence: 99%

“…Luis I. Minchala [16] developed PID-FGS faced aggressive industrial condition like dust and noise environment resulted with RMSE of 0.4247. Similarly, Bastian Dietrich [10] utilized the data was not suitable for energy flexibility and increase in energy cost showed optimization problem in the automation obtained RMSE of 3.607 and Precision of 67 %. In order to overcome the optimization problem occurred in the exisitng methods, Runhai Jiao [7] LSTM-RNN model that failed in considering load forecasting, economic orientations factors for the automation obtained precision of 96.3 % and RMSE of 1.226.…”

Section: Comparative Analysismentioning

confidence: 99%

“…However, in industries the forecasting errors have a considerable influence on Short-term load forecasting is also of great significance for dynamic state estimation and load dispatching [2]. So, IoT is most used domain in IT landscape due to its high heterogeneous integrated technology and high rise of IoT empowered applications in various area for example: Smart cities, E-health system, Smart manufacturing system and Intelligent transportation system [9,10]. The growth of these systems is pressuring the industries to deliver cheaper and personalized products.…”

Section: Introductionmentioning

confidence: 99%

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IoT based Machine Learning Automation Algorithm for Controlling the Industrial Loads

Rao¹,

Chandrakanth²

2021

IJIES

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Presently, industrial automation has become a popular field because of its various advantages includes higher production rates, more efficient use of materials, better product quality, improved safety, and requires minimum labours. This is achieved by utilizing Local Networking Standards (LNSs), remotely monitoring and controlling industrial devices by utilizing Raspberry Pi and Embedded Web Server (EWS) technology. This proposed research provides an idea of utilizing Internet of Things (IoT) for monitoring and controlling the automation process through Wi-Fi or wireless medium by utilizing Raspberry pi as a server system. Additionally, the prediction and error detection utilized in the machine learning process for this Improved Random Forest (IRF) method. The proposed IRF uses Out of Bag (OoB) bagging estimation technique to randomly select sub-datasets for overcoming the optimization problem. The OoB estimation is the technique used to find prediction error in IRF as every samples are not used when each tree in IRF is trained. So for all those bags unused samples estimate the prediction error for a particular bag in prediction process. IRF recursively partition the data into categories and the derived RF model and maps the results. The proposed IRF method overcomes the overfitting problem, receives the command and applies action according to it. The IRF method uses only eight devices to control and monitor more than thirty devices and this entire process is represented as IoT based Machine Learning (ML) Automation Algorithm. The experimental results show that the IRF method provides better outcomes in terms of accuracy, precision, F-measure, and recall. The RMSE values obtained for the proposed IRF model shows 0.0386 lesser error values when compared with the existing models that had achieved RMSE as 3.607 for Long Short Term Memory-Recurrent Neural Network (LSTM-RNN), Artificial Neural Network (ANN) as 1.226 and Fuzzy Gain Scheduling (FGS) of 0.4247.

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