Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting

Byun, Yung-Cheol; Lee, Sang-Joon; Park, Namje

doi:10.3390/en13112681

Cited by 48 publications

(12 citation statements)

References 38 publications

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“…One possible explanation is that the positive and negative predicted values cancel each other out in the RFR algorithm. The ensemble learning algorithms (XGBoost and CatBoost) have shown potential in fields such as biology and medicine [13,[33][34][35][36]. Here, we found that CatBoost turns out to be a promising method for modeling forest AGB using remote sensing data.…”

Section: Comparison Of Model Performancementioning

confidence: 87%

“…GBDT and AdaBoost are commonly used algorithms in Boosting. XGBoost and CatBoost, which are improvements of GBDT, have shown potential in fields such as biology and medicine [33][34][35][36]. Although some studies have attempted to use these methods for modeling forest properties from remote sensing data, the CatBoost method has been introduced very recently [13,37].…”

Section: Introductionmentioning

confidence: 99%

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Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Luo

Wang

Xie

et al. 2021

Forests

126

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Increasing numbers of explanatory variables tend to result in information redundancy and “dimensional disaster” in the quantitative remote sensing of forest aboveground biomass (AGB). Feature selection of model factors is an effective method for improving the accuracy of AGB estimates. Machine learning algorithms are also widely used in AGB estimation, although little research has addressed the use of the categorical boosting algorithm (CatBoost) for AGB estimation. Both feature selection and regression for AGB estimation models are typically performed with the same machine learning algorithm, but there is no evidence to suggest that this is the best method. Therefore, the present study focuses on evaluating the performance of the CatBoost algorithm for AGB estimation and comparing the performance of different combinations of feature selection methods and machine learning algorithms. AGB estimation models of four forest types were developed based on Landsat OLI data using three feature selection methods (recursive feature elimination (RFE), variable selection using random forests (VSURF), and least absolute shrinkage and selection operator (LASSO)) and three machine learning algorithms (random forest regression (RFR), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). Feature selection had a significant influence on AGB estimation. RFE preserved the most informative features for AGB estimation and was superior to VSURF and LASSO. In addition, CatBoost improved the accuracy of the AGB estimation models compared with RFR and XGBoost. AGB estimation models using RFE for feature selection and CatBoost as the regression algorithm achieved the highest accuracy, with root mean square errors (RMSEs) of 26.54 Mg/ha for coniferous forest, 24.67 Mg/ha for broad-leaved forest, 22.62 Mg/ha for mixed forests, and 25.77 Mg/ha for all forests. The combination of RFE and CatBoost had better performance than the VSURF–RFR combination in which random forests were used for both feature selection and regression, indicating that feature selection and regression performed by a single machine learning algorithm may not always ensure optimal AGB estimation. It is promising to extending the application of new machine learning algorithms and feature selection methods to improve the accuracy of AGB estimates.

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Section: Comparison Of Model Performancementioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Luo

Wang

Xie

et al. 2021

Forests

126

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“…Their results, based on a performance index that combines coefficient of determination (R 2 ), RMSE, and MAPE (Mean Absolute Percentage Error), indicate that the random forest performance was 14-25% and 5-5.5% better than RT and SVR, respectively [11]. In 2020, Khan et al proposed hybrid machine learning methods [12][13][14] to forecast the energy demand. They developed a hybrid algorithm based on three machine learning techniques, i.e., random forest, extreme gradient boosting, and categorical boosting for energy demand forecasting.…”

Section: Previous Workmentioning

confidence: 99%

Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning

Shirzadi

Nizami

Khazen

et al. 2021

Designs

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Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has become a crucial need. Such forecasts are necessary to support the plans and decisions related to the capacity evaluation of centralized and decentralized power generation systems, demand response strategies, and controlling the operation. To address this problem, the main objective of this study is to develop and compare precise district level models for predicting the electrical load demand based on machine learning techniques including support vector machine (SVM) and Random Forest (RF), and deep learning methods such as non-linear auto-regressive exogenous (NARX) neural network and recurrent neural networks (Long Short-Term Memory—LSTM). A dataset including nine years of historical load demand for Bruce County, Ontario, Canada, fused with the climatic information (temperature and wind speed) are used to train the models after completing the preprocessing and cleaning stages. The results show that by employing deep learning, the model could predict the load demand more accurately than SVM and RF, with an R-Squared of about 0.93–0.96 and Mean Absolute Percentage Error (MAPE) of about 4–10%. The model can be used not only by the municipalities as well as utility companies and power distributors in the management and expansion of electricity grids; but also by the households to make decisions on the adoption of home- and district-scale renewable energy technologies.

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“…However, electric consumption forecasting is a challenging task due to various influencing factors such as outdoor weather conditions, size of the building, number of occupants, the economic state and comfort index of the occupant, the use of heating, ventilation, and air conditioning components (HVAC), operating schedules, and energy usage pattern of the occupant [3,4]. Electric energy consumption prediction is comprised of various temporal scales to analyze consumer electricity demands, such as hourly, sub-hourly, daily, weekly, monthly, yearly, and seasonally (Summer, Winter, and Fall) [5]. Therefore, it is beneficial to utilize data analytics to process and analyze the hidden characteristics of electrical energy consumption data that is essential for understanding the energy demands of multifamily residential buildings.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

et al. 2021

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With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

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Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting

Cited by 48 publications

References 38 publications

Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning

Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

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