Electricity load forecasting using advanced feature selection and optimal deep learning model for the variable refrigerant flow systems

Kim, Woohyun; Han, Yerim; Kim, Kyoung Jae; Song, Kisun

doi:10.1016/j.egyr.2020.09.019

Cited by 37 publications

(13 citation statements)

References 18 publications

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“…However, an input space with redundancy and many intercorrelated features typically decreases the accuracy of the prediction model and contributes more to the over-fitting problem. To avoid overfitting, two feature selection methods, which have been proposed in the literature, including Pearson Correlation Coefficient (PCC) [27], [28] and Recursive Feature Elimination Technique (RFE) [29], [30], [31] were used to reduce the dimension of input space.…”

Section: ) Feature Selectionmentioning

confidence: 99%

An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities

et al. 2021

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This paper addresses the estimation of household communities' overall energy usage and solar energy production, considering different prediction horizons. Forecasting the electricity demand and energy generation of communities can help enrich the information available to energy grid operators to better plan their short-term supply. Moreover, households will increasingly need to know more about their usage and generation patterns to make wiser decisions on their appliance usage and energy-trading programs. The main issues to address here are the volatility of load consumption induced by the consumption behaviour and variability in solar output influenced by solar cells specifications, several meteorological variables, and contextual factors such as time and calendar information. To address these issues, we propose a predicting approach that first considers the highly influential factors and, second, benefits from an ensemble learning method where one Gradient Boosted Regression Tree algorithm is combined with several Sequenceto-Sequence LSTM networks. We conducted experiments on a public dataset provided by the Ausgrid Australian electricity distributor collected over three years. The proposed model's prediction performance was compared to those by contributing learners and by conventional ensembles. The obtained results have demonstrated the potential of the proposed predictor to improve short-term multi-step forecasting by providing more stable forecasts and more accurate estimations under different day types and meteorological conditions.

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Section: ) Feature Selectionmentioning

confidence: 99%

An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities

et al. 2021

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“…Spark Architecture. In load forecasting, the training data mainly includes load data and meteorological data [27,28]. For traditional shallow learning methods, on the one hand, due to the simple structure model, it is difficult to learn the complex nonlinear mapping relationship in the training data.…”

Section: Load Forecasting Based On Improved Deep Learning Inmentioning

confidence: 99%

Load Forecasting Method Based on Improved Deep Learning in Cloud Computing Environment

Zhang

Guo

Feng

et al. 2021

Scientific Programming

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For the problems of low accuracy and low efficiency of most load forecasting methods, a load forecasting method based on improved deep learning in cloud computing environment is proposed. Firstly, the preprocessed data set is divided into several data partitions with relatively balanced data volume through spatial grid, so as to better detect abnormal data. Then, the density peak clustering algorithm based on spark is used to detect abnormal data in each partition, and the local clusters and abnormal points are merged. The parallel processing of data is realized by using spark cluster computing platform. Finally, the deep belief network is used for load classification, and the classification results are input into the empirical mode decomposition-gating recurrent unit network model, and the load prediction results are obtained through learning. Based on the load data of a power grid, the experimental results demonstrate that the mean prediction error of the proposed method is basically controlled within 3% in the short term and 0.023 MW, 19.75%, and 2.76% in the long term, which are better than other comparison methods, and the parallel performance is good, which has a certain feasibility.

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“…Attribute selection is substantial step in order to avoid over-fitting and detect the best features. Feature selection is widely used in traditional machine learning based models [17][18][19][20][21][22], as well as in deep learning based models [23][24][25][26][27][28].…”

Section: Attribute Selectionmentioning

confidence: 99%

Feature Selection Based Data Mining Approach for Coronary Artery Disease Diagnosis

Akyol

2021

Academic Platform Journal of Engineering and Science

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Cardiovascular diseases responsible for many deaths are very common and important health problems. According to World Health Organization, each year 17.7 million people die because of them. Coronary artery disease is the most important type of cardiovascular diseases that cause serious heart problems in patients, affecting the heart's function negatively. Being aware of the important attributes for this disease will help field-specialist in the analysis of routine laboratory test results of a patient coming internal medicine or another medicine unit except for the cardiology unit. In this study, it is aimed to determine the significance of attributes for coronary artery disease by utilizing Stability Selection method.

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Electricity load forecasting using advanced feature selection and optimal deep learning model for the variable refrigerant flow systems

Cited by 37 publications

References 18 publications

An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities

An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities

Load Forecasting Method Based on Improved Deep Learning in Cloud Computing Environment

Feature Selection Based Data Mining Approach for Coronary Artery Disease Diagnosis

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