Short-Term Load Forecasting Based on Elastic Net Improved GMDH and Difference Degree Weighting Optimization

Liu, Wei; Dou, Zheng; Wang, Weiguo; Liu, Yueyu; Zou, Hao; Zhang, Bo; Hou, Shoujun

doi:10.3390/app8091603

Cited by 16 publications

(9 citation statements)

References 50 publications

(57 reference statements)

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“…New layers ae created by repeating this step until there is stability in the error criterion. Previous studies have provided detailed description of the GMDH model [29]- [31]. The Volterra functional series of the Kolmogorov-Gabor polynomial expresses the general input-output relationship in the GMDH algorithm as follows [32]:…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

On the Evaluation of the Gradient Tree Boosting Model for Groundwater Level Forecasting

Naganna

Beyaztaş

Bokde

et al. 2020

Kbes

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Though groundwater is a replenishable resource, it’s over exploitation has posed greater problem of its depletion. Hence, monitoring and forecasting of groundwater levels has become a primary task of governmental water boards/agencies for sustainable water management. The current study focused on evaluating the performance of Gradient Tree Boosting (GTB) model with that of conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) and Group Method of Data Handling (GMDH) models in forecasting groundwater levels of two coastal aquifers. Data of two groundwater level monitoring wells penetrating into unconfined aquifers located at Shirtadi and Rayee near to Mangalore city of Karnataka state, India was considered in the present study. Monthly groundwater level data of the years 2000 – 2013 were used for model simulation; wherein 70% of data was used for model training and the remaining 30% served as testing data. Comparative result evaluation shows that the proposed GTB approach for one month ahead groundwater level forecasting was giving much accurate results than the other models for the same period of time and same set of data. For Rayee monitoring well, the error statistic, RRMSE of GTB, GMDH and ANFIS models obtained during test phase were 0.473, 0.517 and 0.7522, respectively. The comparison is examined further with different performance metrics.

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Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

On the Evaluation of the Gradient Tree Boosting Model for Groundwater Level Forecasting

Naganna

Beyaztaş

Bokde

et al. 2020

Kbes

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“…In the framework of the research project i-DREAMS, [ 9 ] propose the prediction of continuous indicators of risk such as the time spent at each safety level in order to tune the frequency of warnings triggered to the driver in real-time. Although to our knowledge, a similar development of the above approach has not been found in research, a similar methodology is applied to short-term traffic prediction problems [ 26 , 27 , 28 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Estimation of a Driving Safety Tolerance Zone Using Imbalanced Machine Learning

Garefalakis

Katrakazas

Yannis

2022

Sensors

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Predicting driving behavior and crash risk in real-time is a problem that has been heavily researched in the past years. Although in-vehicle interventions and gamification features in post-trip dashboards have emerged, the connection between real-time driving behavior prediction and the triggering of such interventions is yet to be realized. This is the focus of the European Horizon2020 project “i-DREAMS”, which aims at defining, developing, testing and validating a ‘Safety Tolerance Zone’ (STZ) in order to prevent drivers from risky driving behaviors using interventions both in real-time and post-trip. However, the data-driven conceptualization of STZ levels is a challenging task, and data class imbalance might hinder this process. Following the project principles and taking the aforementioned challenges into consideration, this paper proposes a framework to identify the level of risky driving behavior as well as the duration of the time spent in each risk level by private car drivers. This aim is accomplished by four classification algorithms, namely Support Vector Machines (SVMs), Random Forest (RFs), AdaBoost, and Multilayer Perceptron (MLP) Neural Networks and imbalanced learning using the Adaptive Synthetic technique (ADASYN) in order to deal with the unbalanced distribution of the dataset in the STZ levels. Moreover, as an alternative approach of risk prediction, three regression algorithms, namely Ridge, Lasso, and Elastic Net are used to predict time duration. The results showed that RF and MLP outperformed the rest of the classifiers with 84% and 82% overall accuracy, respectively, and that the maximum speed of the vehicle during a 30 s interval, is the most crucial predictor for identifying the driving time at each safety level.

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“…Reference [33] uses Bayesian Ridge Regression for forecasting wind speed and direction 1 to 24 hours ahead. Reference [34] proposes the group method of data handling based on the Elastic Net in order to forecasting the load of a power grid. Although the works in this subcategory select features during the training of the forecaster, some parameters of these methods have to be set before training.…”

Section: Relationship To the State Of The Artmentioning

confidence: 99%

An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things

et al. 2021

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We develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based, wrapper-based and embedded feature selection methods, our architecture enables the automatic selection of features dynamically based on feature importance score calculation and gamma-gated feature selection units that are trained jointly and end-to-end with the forecaster. We compare the performance of our FSF architecture on the problem of forecasting IoT device traffic against the following existing (feature selection, forecasting) technique pairs: Autocorrelation Function (ACF), Analysis of Variance (ANOVA), Recurrent Feature Elimination (RFE) and Ridge Regression methods for feature selection, and Linear Regression, Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM), 1 Dimensional Convolutional Neural Network (1D CNN), Autoregressive Integrated Moving Average (ARIMA), and Logistic Regression for forecasting. We show that our FSF architecture achieves either the best or close to the best performance among all of the competing techniques by virtue of its dynamic, automatic feature selection capability. In addition, we demonstrate that both the training time and the execution time of FSF are reasonable for IoT applications. This work represents a milestone for the development of predictive networks for IoT in smart cities of the near future.

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Short-Term Load Forecasting Based on Elastic Net Improved GMDH and Difference Degree Weighting Optimization

Cited by 16 publications

References 50 publications

On the Evaluation of the Gradient Tree Boosting Model for Groundwater Level Forecasting

On the Evaluation of the Gradient Tree Boosting Model for Groundwater Level Forecasting

Data-Driven Estimation of a Driving Safety Tolerance Zone Using Imbalanced Machine Learning

An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things

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