Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression

Phyo, Pyae-Pyae; Byun, Yung-Cheol; Park, Namje

doi:10.3390/sym14010160

Cited by 41 publications

(21 citation statements)

References 44 publications

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“…LSTMs are susceptible to specific initializations of activation functions (Li [21]). Phyo et al [22] suggested the utilization of machine learning algorithms with reduced error rates that are taught to create the planned voting regressor model, which is essential for energy producers in order to meet the needed quantity of energy between consumption and supply. The classification of data set is more complex by using this model.…”

Section: Literature Surveymentioning

confidence: 99%

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

Jaharudin

Song

2022

Journal of Sensors

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Consumption behavior prediction reveals customer attributes, personal preferences, and intrinsic laws. Organizations would benefit from knowing further about customer needs and business desires by monitoring client behavior to provide more precise recommendations and boost acquisition rates. The economics of the customer, buyer groupings, and product quality are only a few of the numerous variables that influence customer behavior. The key issue that has to be resolved at this time is how to filter out useful information from these vast amounts of data to forecast customer behavior. For customer consumption behavior prediction and analysis with an advanced quantitative research process, we proposed the multiobjective evolutionary algorithm, which significantly boosts the accuracy of consumption behavior predictions. The dataset is initially gathered based on consumer preferences and behaviors as the essential information for the entire prediction model. Min-max normalization is used as a component of the preprocessing of the data to get the elimination of redundant and superfluous data. The Word2vec model is utilized for feature extraction, and boosted ant colony optimization (BACO) is employed to choose the best features. Utilizing the suggested multiobjective evolutionary algorithm (MOEA), the predictions are made. The suggested system’s performance is assessed, and the metrics are contrasted with more established methods. The findings demonstrate that the suggested MOEA technique performs well than the traditional ML, XGB, AI, and HNB algorithm methods in terms of accuracy (95 percent), quality of prediction (97 percent), precision (99 percent), recall (93 percent), F 1 -score (98 percent), and prediction time (50 seconds). Hence, the outcomes show that the regression model is sustainable. The suggested consumption behavior prediction system has demonstrated its efficiency in boosting profitability.

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Section: Literature Surveymentioning

confidence: 99%

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

Jaharudin

Song

2022

Journal of Sensors

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“…Based on the closest distance, the most related K value is chosen to categorize the input features. The KNN algorithms rely on the voting function of the chosen ideal k value and distance [40,42,58,68,69]. The KNN techniques have been employed in various studies with a significant level of accuracy, including one where short-term energy forecasted was performed with a mean absolute percentage error (MAPE) of 4% [68] and the estimation of chlorophyll-a and suspended solids concentration in water bodies in Brazil with R 2 > 80%.…”

Section: Standalone ML Techniquesmentioning

confidence: 99%

“…Hyperparameters for the GBM include the number of iterations, the learning rate, the loss function, and the maximum depth of the single weak models. The boosting model consists of several logistic regression or decision trees built by relying on some randomly chosen trees, with its performance improved by iteratively adding new randomly chosen decision trees which help to improve the accuracy of the previous iteration model [57,68,76].…”

Section: Ensemble ML Techniquesmentioning

confidence: 99%

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

Adjovu

Stephen

Ahmad

2023

Water

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Total dissolved solids (TDS) concentration determination in water bodies is sophisticated, time-consuming, and involves expensive field sampling and laboratory processes. TDS concentration has, however, been linked to electrical conductivity (EC) and temperature. Compared to monitoring TDS concentrations, monitoring EC and temperature is simpler, inexpensive, and takes less time. This study, therefore, applied several machine learning (ML) approaches to estimate TDS concentration in Lake Mead using EC and temperature data. Standalone models including the support vector machine (SVM), linear regressors (LR), K-nearest neighbor model (KNN), the artificial neural network (ANN), and ensemble models such as bagging, gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), and extra trees (ET) models were used in this study. The models’ performance were evaluated using several performance metrics aimed at providing a holistic assessment of each model. Metrics used include the coefficient of determination (R2), mean absolute error (MAE), percent mean absolute relative error (PMARE), root mean square error (RMSE), the scatter index (SI), Nash–Sutcliffe model efficiency (NSE) coefficient, and percent bias (PBIAS). Results obtained showed varying model performance at the training, testing, and external validation stage of the models, with obtained R2 of 0.77–1.00, RMSE of 2.28–37.68 mg/L, an MAE of 0.14–22.67 mg/L, a PMARE of 0.02–3.42%, SI of 0.00–0.06, NSE of 0.77–1.00, and a PBIAS of 0.30–0.97 across all models for the three datasets. We utilized performance rankings to assess the model performance and found the LR to be the best-performing model on the external validation datasets among all the models (R2 of 0.82 and RMSE of 33.09 mg/L), possibly due to the established existence of a relationship between TDS and EC, although this may not always be linear. Similarly, we found the XGBoost to be the best-performing ensemble model based on the external validation with R2 of 0.81 and RMSE of 34.19 mg/L. Assessing the overall performance of the models across all the datasets, however, revealed GBM to produce a superior performance based on the ranks, possibly due to its ability to reduce overfitting and improve generalizations. The findings from this study could be employed in assisting water resources managers and stakeholders in effective monitoring and management of water resources to ensure their sustainability.

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“…At this point, MW prediction has problems such as limited data and many parameters affecting the amount of MW, so there is a need for more powerful algorithms, such as ensemble methods based on ML algorithms, to handle these problems. However, to the best of our knowledge, no investigations have been undertaken utilizing the random forest (RF), gradient boosting machine (GBM), and adaptive boosting (AdaBoost) algorithms to predict MW amounts, nor any utilizing ensemble methods based on ML algorithms, which are considered to represent a better approach than single algorithms [25][26][27][28][29][30]. Instead of using single machine learning algorithms, this study proposed ensemble voting regression (VR) algorithms based on machine learning to obtain better predictions of MW generation in ˙Istanbul, the largest city in Turkey, utilizing official data.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey

Rouyendegh

Devrim-İçtenbaş

2022

Mathematics

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Predicting medical waste (MW) properly is vital for an effective waste management system (WMS), but it is difficult because of inadequate data and various factors that impact MW. This study’s primary objective was to develop an ensemble voting regression algorithm based on machine learning (ML) algorithms such as random forests (RFs), gradient boosting machines (GBMs), and adaptive boosting (AdaBoost) to predict the MW for Istanbul, the largest city in Turkey. This was the first study to use ML algorithms to predict MW, to our knowledge. First, three ML algorithms were developed based on official data. To compare their performances, performance measures such as mean absolute deviation (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-squared) were calculated. Among the standalone ML models, RF achieved the best performance. Then, these base models were used to construct the proposed ensemble voting regression (VR) model utilizing weighted averages according to the base models’ performances. The proposed model outperformed three baseline models, with the lowest RMSE (843.70). This study gives an effective tool to practitioners and decision-makers for planning and constructing medical waste management systems by predicting the MW quantity.

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Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression

Cited by 41 publications

References 44 publications

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey

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