Electricity Load Forecasting in Thailand Using Deep Learning Models

Phyo, Pyae Pyae; Jeenanunta, Chawalit; Hashimoto, Kiyota

doi:10.18178/ijeetc.8.4.221-225

Cited by 40 publications

(11 citation statements)

References 17 publications

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“…The forecasts of the proposed model are discussed and compared with the results of the six benchmark models. Correspondingly, the comparisons of monthly MAPE, MAE, and MSE are indicated in Tables 3-5 2 , respectively. Therefore, all ML models generally provided adaptable accuracy performance because five ML benchmark models were chosen from the top performing among seventeen ML models during the training process.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, it also helps reduce operating and generating costs and conduct shortterm scheduling functions in the power system. Forecasting can be divided into three terms based on predictive duration: short term, medium term, and long term [2]. This research mainly focuses on short-term energy forecasting because it considers next-hour prediction using hourly energy data.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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Meeting the required amount of energy between supply and demand is indispensable for energy manufacturers. Accordingly, electric industries have paid attention to short-term energy forecasting to assist their management system. This paper firstly compares multiple machine learning (ML) regressors during the training process. Five best ML algorithms, such as extra trees regressor (ETR), random forest regressor (RFR), light gradient boosting machine (LGBM), gradient boosting regressor (GBR), and K neighbors regressor (KNN) are trained to build our proposed voting regressor (VR) model. Final predictions are performed using the proposed ensemble VR and compared with five selected ML benchmark models. Statistical autoregressive moving average (ARIMA) is also compared with the proposed model to reveal results. For the experiments, usage energy and weather data are gathered from four regions of Jeju Island. Error measurements, including mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) are computed to evaluate the forecasting performance. Our proposed model outperforms six baseline models in terms of the result comparison, giving a minimum MAPE of 0.845% on the whole test set. This improved performance shows that our approach is promising for symmetrical forecasting using time series energy data in the power system sector.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2022

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“…Zhang et al [21] proposed a k-medoids clustering approach to remove the effect of outliers and noise. A local regression filtering technique can also be used to remove outliers [29]. However, Zheng et al [30] replaced the outliers with an average value of adjacent points.…”

Section: Techniques To Deal With Outliers Noise and Missing Valuesmentioning

confidence: 99%

Energy Demand Forecasting and Optimizing Electric Systems for Developing Countries

et al. 2023

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under Grant 56.00.0000. 028.20.0005.19-440; and in part by the Innovation Design and Entrepreneurship Academy, (iDEA) project of Bangladesh Computer Council (BCC), Bangladesh under Grant Mujib1002021/680. We thank our colleagues from Advanced Intelligent Multidisciplinary Systems (AIMS) Laboratory, United International University (UIU), Bangladesh who provided insights and expertise that greatly assisted the research.

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“…Secondly, calendar effects consist of day type, month, public holiday, and national holiday. Finally, weather effects are typically associated with meteorological circumstances such as temperature, cloudy, and humidity [9]. As a result, this study primarily considers the weather information, including temperature, dew point, humidity, wind speed, solar radiation, and other factors for better understanding the non-linear relationship between load patterns and influential variables that can enhance energy forecasting performance.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

Phyo

Byun

2021

Symmetry

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The energy manufacturers are required to produce an accurate amount of energy by meeting the energy requirements at the end-user side. Consequently, energy prediction becomes an essential role in the electric industrial zone. In this paper, we propose the hybrid ensemble deep learning model, which combines multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM to improve the forecasting performance. These DL architectures are more popular and better than other machine learning (ML) models for time series electrical load prediction. Therefore, hourly-based energy data are collected from Jeju Island, South Korea, and applied for forecasting. We considered external features associated with meteorological conditions affecting energy. Two-year training and one-year testing data are preprocessed and arranged to reform the times series, which are then trained in each DL model. The forecasting results of the proposed ensemble model are evaluated by using mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Error metrics are compared with DL stand-alone models such as MLP, CNN, LSTM, and CNN-LSTM. Our ensemble model provides better performance than other forecasting models, providing minimum MAPE at 0.75%, and was proven to be inherently symmetric for forecasting time-series energy and demand data, which is of utmost concern to the power system sector.

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Electricity Load Forecasting in Thailand Using Deep Learning Models

Cited by 40 publications

References 17 publications

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

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

Energy Demand Forecasting and Optimizing Electric Systems for Developing Countries

Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

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