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Electricity is one of the critical role players to build an economy. Electricity consumption and generation can affect the overall policy of the country. Such importance opens an area for intelligent systems that can provide future insights. Intelligent management for electric power consumption requires future electricity power consumption prediction with less error. These predictions provide insights for making decisions to smooth line the policy and grow the country’s economy. Future prediction can be categorized into three categories, namely (1) Long-Term, (2) Short-Term, and (3) Mid-Term predictions. For our study, we consider the Mid-Term electricity consumption prediction. Dataset provided by Korea Electric power supply to get insights for a metropolitan city like Seoul. Dataset is in time-series, so statistical and machine learning models can be used. This study provides experimental results from the proposed ARIMA and CNN-Bi-LSTM. Hyperparameters are tuned for ARIMA and neural network models to increase the models’ accuracy, which looks promising as RMSE for training is 0.14 and 0.20 RMSE for testing.
M. Junaid Gul; Gul Malik Urfa; Anand Paul; Jihoon Moon; Seungmin Rho; Eenjun Hwang. Mid-term electricity load prediction using CNN and Bi-LSTM. The Journal of Supercomputing 2021, 1 -17.
AMA StyleM. Junaid Gul, Gul Malik Urfa, Anand Paul, Jihoon Moon, Seungmin Rho, Eenjun Hwang. Mid-term electricity load prediction using CNN and Bi-LSTM. The Journal of Supercomputing. 2021; ():1-17.
Chicago/Turabian StyleM. Junaid Gul; Gul Malik Urfa; Anand Paul; Jihoon Moon; Seungmin Rho; Eenjun Hwang. 2021. "Mid-term electricity load prediction using CNN and Bi-LSTM." The Journal of Supercomputing , no. : 1-17.
Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.
Seungmin Jung; Jihoon Moon; Sungwoo Park; Eenjun Hwang. An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting. Sensors 2021, 21, 1639 .
AMA StyleSeungmin Jung, Jihoon Moon, Sungwoo Park, Eenjun Hwang. An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting. Sensors. 2021; 21 (5):1639.
Chicago/Turabian StyleSeungmin Jung; Jihoon Moon; Sungwoo Park; Eenjun Hwang. 2021. "An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting." Sensors 21, no. 5: 1639.
Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods.
Jinwoong Park; Jihoon Moon; Seungmin Jung; Eenjun Hwang. Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island. Remote Sensing 2020, 12, 2271 .
AMA StyleJinwoong Park, Jihoon Moon, Seungmin Jung, Eenjun Hwang. Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island. Remote Sensing. 2020; 12 (14):2271.
Chicago/Turabian StyleJinwoong Park; Jihoon Moon; Seungmin Jung; Eenjun Hwang. 2020. "Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island." Remote Sensing 12, no. 14: 2271.
Biodiversity conservation is important for the protection of ecosystems. One key task for sustainable biodiversity conservation is to effectively preserve species’ habitats. However, for various reasons, many of these habitats have been reduced or destroyed in recent decades. To deal with this problem, it is necessary to effectively identify potential habitats based on habitat suitability analysis and preserve them. Various techniques for habitat suitability estimation have been proposed to date, but they have had limited success due to limitations in the data and models used. In this paper, we propose a novel scheme for assessing habitat suitability based on a two-stage ensemble approach. In the first stage, we construct a deep neural network (DNN) model to predict habitat suitability based on observations and environmental data. In the second stage, we develop an ensemble model using various habitat suitability estimation methods based on observations, environmental data, and the results of the DNN from the first stage. For reliable estimation of habitat suitability, we utilize various crowdsourced databases. Using observational and environmental data for four amphibian species and seven bird species in South Korea, we demonstrate that our scheme provides a more accurate estimation of habitat suitability compared to previous other approaches. For instance, our scheme achieves a true skill statistic (TSS) score of 0.886, which is higher than other approaches (TSS = 0.725 ± 0.010).
Jehyeok Rew; Yongjang Cho; Jihoon Moon; Eenjun Hwang. Habitat Suitability Estimation Using a Two-Stage Ensemble Approach. Remote Sensing 2020, 12, 1475 .
AMA StyleJehyeok Rew, Yongjang Cho, Jihoon Moon, Eenjun Hwang. Habitat Suitability Estimation Using a Two-Stage Ensemble Approach. Remote Sensing. 2020; 12 (9):1475.
Chicago/Turabian StyleJehyeok Rew; Yongjang Cho; Jihoon Moon; Eenjun Hwang. 2020. "Habitat Suitability Estimation Using a Two-Stage Ensemble Approach." Remote Sensing 12, no. 9: 1475.
For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to diverse reasons, such as device malfunctions and signal transmission errors, missing data are frequently observed in the actual data. Previously, many imputation methods have been proposed to compensate for missing values; however, these methods have achieved limited success in imputing electric energy consumption data because the period of data missing is long and the dependency on historical data is high. In this study, we propose a novel missing-value imputation scheme for electricity consumption data. The proposed scheme uses a bagging ensemble of multilayer perceptrons (MLPs), called softmax ensemble network, wherein the ensemble weight of each MLP is determined by a softmax function. This ensemble network learns electric energy consumption data with explanatory variables and imputes missing values in this data. To evaluate the performance of our scheme, we performed diverse experiments on real electric energy consumption data and confirmed that the proposed scheme can deliver superior performance compared to other imputation methods.
Seungwon Jung; Jihoon Moon; Sungwoo Park; Seungmin Rho; Sung Wook Baik; Eenjun Hwang. Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation. Sensors 2020, 20, 1772 .
AMA StyleSeungwon Jung, Jihoon Moon, Sungwoo Park, Seungmin Rho, Sung Wook Baik, Eenjun Hwang. Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation. Sensors. 2020; 20 (6):1772.
Chicago/Turabian StyleSeungwon Jung; Jihoon Moon; Sungwoo Park; Seungmin Rho; Sung Wook Baik; Eenjun Hwang. 2020. "Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation." Sensors 20, no. 6: 1772.
An energy-management system requires accurate prediction of the electric load for optimal energy management. However, if the amount of electric load data is insufficient, it is challenging to perform an accurate prediction. To address this issue, we propose a novel electric load forecasting scheme using the electric load data of diverse buildings. We first divide the electric energy consumption data into training and test sets. Then, we construct multivariate random forest (MRF)-based forecasting models according to each building except the target building in the training set and a random forest (RF)-based forecasting model using the limited electric load data of the target building in the test set. In the test set, we compare the electric load of the target building with that of other buildings to select the MRF model that is the most similar to the target building. Then, we predict the electric load of the target building using its input variables via the selected MRF model. We combine the MRF and RF models by considering the different electric load patterns on weekdays and holidays. Experimental results demonstrate that combining the two models can achieve satisfactory prediction performance even if the electric data of only one day are available for the target building.
Jihoon Moon; Junhong Kim; Pilsung Kang; Eenjun Hwang. Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods. Energies 2020, 13, 886 .
AMA StyleJihoon Moon, Junhong Kim, Pilsung Kang, Eenjun Hwang. Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods. Energies. 2020; 13 (4):886.
Chicago/Turabian StyleJihoon Moon; Junhong Kim; Pilsung Kang; Eenjun Hwang. 2020. "Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods." Energies 13, no. 4: 886.
Smart grid systems, which have gained much attention due to its ability to reduce operation and management costs of power systems, consist of diverse components including energy storage, renewable energy, and combined cooling, heating and power (CCHP) systems. The CCHP has been investigated to reduce energy costs by using the thermal energy generated during the power generation process. For efficient utilization of CCHP and numerous power generation systems, accurate short-term load forecasting (STLF) is necessary. So far, even though many single algorithm-based STLF models have been proposed, they showed limited success in terms of applicability and coverage. This problem can be alleviated by combining such single algorithm-based models in ways that take advantage of their strengths. In this paper, we propose a novel two-stage STLF scheme; extreme gradient boosting and random forest models are executed in the first stage, and deep neural networks are executed in the second stage to combine them. To show the effectiveness of our proposed scheme, we compare our model with other popular single algorithm-based forecasting models and then show how much electric charges can be saved by operating CCHP based on the schedules made by the economic analysis on the predicted electric loads.
Sungwoo Park; Jihoon Moon; Seungwon Jung; Seungmin Rho; Sung Wook Baik; Eenjun Hwang. A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling. Energies 2020, 13, 443 .
AMA StyleSungwoo Park, Jihoon Moon, Seungwon Jung, Seungmin Rho, Sung Wook Baik, Eenjun Hwang. A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling. Energies. 2020; 13 (2):443.
Chicago/Turabian StyleSungwoo Park; Jihoon Moon; Seungwon Jung; Seungmin Rho; Sung Wook Baik; Eenjun Hwang. 2020. "A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling." Energies 13, no. 2: 443.
A stable power supply is very important in the management of power infrastructure. One of the critical tasks in accomplishing this is to predict power consumption accurately, which usually requires considering diverse factors, including environmental, social, and spatial-temporal factors. Depending on the prediction scope, building type can also be an important factor since the same types of buildings show similar power consumption patterns. A university campus usually consists of several building types, including a laboratory, administrative office, lecture room, and dormitory. Depending on the temporal and external conditions, they tend to show a wide variation in the electrical load pattern. This paper proposes a hybrid short-term load forecast model for an educational building complex by using random forest and multilayer perceptron. To construct this model, we collect electrical load data of six years from a university campus and split them into training, validation, and test sets. For the training set, we classify the data using a decision tree with input parameters including date, day of the week, holiday, and academic year. In addition, we consider various configurations for random forest and multilayer perceptron and evaluate their prediction performance using the validation set to determine the optimal configuration. Then, we construct a hybrid short-term load forecast model by combining the two models and predict the daily electrical load for the test set. Through various experiments, we show that our hybrid forecast model performs better than other popular single forecast models.
Jihoon Moon; Yongsung Kim; Minjae Son; Eenjun Hwang. Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron. Energies 2018, 11, 3283 .
AMA StyleJihoon Moon, Yongsung Kim, Minjae Son, Eenjun Hwang. Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron. Energies. 2018; 11 (12):3283.
Chicago/Turabian StyleJihoon Moon; Yongsung Kim; Minjae Son; Eenjun Hwang. 2018. "Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron." Energies 11, no. 12: 3283.
Recently, Internet of Things (IoT) technology has become a hot trend and is used in a wide variety of fields. For instance, in education, this technology contributes to improving learning efficiency in the class by enabling learners to interact with physical devices and providing appropriate learning content based on this interaction. Such interaction data can be collected through the physical devices to define personal data. In the meanwhile, multimedia contents in this environment usually have a wide variety of formats and standards, making it difficult for computers to understand their meaning and reuse them. This could be a serious obstacle to the effective use or sustainable management of educational contents in IoT-based educational systems. In order to solve this problem, in this paper, we propose a semantic annotation scheme for sustainable computing in the IoT environment. More specifically, we first show how to collect appropriate multimedia contents and interaction data. Next, we calculate the readability of learning materials and define the user readability level to provide appropriate contents to the learners. Finally, we describe our semantic annotation scheme and show how to annotate collected data using our scheme. We implement a prototype system and show that our scheme can achieve efficient management of various learning materials in the IoT-based educational system.
Yongsung Kim; Jihoon Moon; Eenjun Hwang. Constructing Differentiated Educational Materials Using Semantic Annotation for Sustainable Education in IoT Environments. Sustainability 2018, 10, 1296 .
AMA StyleYongsung Kim, Jihoon Moon, Eenjun Hwang. Constructing Differentiated Educational Materials Using Semantic Annotation for Sustainable Education in IoT Environments. Sustainability. 2018; 10 (4):1296.
Chicago/Turabian StyleYongsung Kim; Jihoon Moon; Eenjun Hwang. 2018. "Constructing Differentiated Educational Materials Using Semantic Annotation for Sustainable Education in IoT Environments." Sustainability 10, no. 4: 1296.