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As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid. [ABSTRACT FROM AUTHOR]

In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model, and Long Short-Term Memory (LSTM) model. These algorithms were applied in Period 1 (prior to the onset of the COVID-19 pandemic) and Period 2 (after the onset of the COVID-19 pandemic). Period 1 was characterized by an upward trend in energy consumption, while Period 2 showed a reduction in energy consumption. LSTM performed best in its prediction power specifically in Period 1, and RF outperformed the other models in Period 2. Findings, therefore, suggested the applicability of machine learning to forecast energy consumption and also demonstrated that traditional econometric approaches may outperform machine learning when there is less unknown irregularity in the time series, but machine learning can work better with unexpected irregular time series data. [ABSTRACT FROM AUTHOR]

The most important thing to operate a power system is that the power supply should be close to the power demand. In order to predict the amount of electric power transaction (EPT), it is important to choose and decide the variable and its starting date. In this paper, variables that could be acquired one the starting day of prediction were chosen. This paper designated date, temperature and special day as variables to predict the amount of EPT of the Korea Electric Power company. This paper also used temperature data from a year ago to predict the next year. To do this, we proposed single deep learning algorithms and hybrid deep learning algorithms. The former included multi-layer perceptron (MLP), convolution neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), support vector machine regression (SVR), and adaptive network-based fuzzy inference system (ANFIS). The latter included LSTM + CNN and CNN + LSTM. We then confirmed the improvement of accuracy for prediction using pre-processed variables compared to original variables We also assigned two years of test data during 2017–2018 as variable data to measure high prediction accuracy. We then selected a high-accuracy algorithm after measuring root mean square error (RMSE) and mean absolute percent error (MAPE). Finally, we predicted the amount of EPT in 2018 and then measured the error for each proposed algorithm. With these acquired error data, we obtained a model for predicting the amount of EPT with a high accuracy. [ABSTRACT FROM AUTHOR]

The energy-intensive steel industry, which consumes substantial amounts of electricity, meets its power demands through external electricity purchases and self-generation through the operation of its own generators. This study aimed to optimize boiler combustion efficiency and increase power generation output by deriving optimal operational values for O2 and CO within the boiler flue gas using machine learning (ML) with the aim of achieving maximum boiler efficiency. This study focuses on the power-generation boilers at steel mill P in Korea. First, 361 types of operation data from power generation equipment were collected and preprocessed. Subsequently, a partial least squares regression (PLSR) algorithm was used to develop a prediction model for O2 and CO values, known as the Boiler Flue Gas Prediction Model (BFG-PM). The prediction accuracy for O2 was notably high (83.2%), whereas that for CO was lower (53.4%). Nonetheless, the model's reliability was high because more than 90% of the predicted values were within a 10% error range. Finally, the correlation of the BFG-PM model was applied to the performance test code (PTC) 4.0 for the boiler efficiency calculations formula, deriving the optimal O2 and CO control points. Through a simulation, it was verified that the boiler efficiency was improved by controlling the combustion air. In addition, an average increase in boiler efficiency of 0.29% was confirmed by applying it directly to the generator operating on-site. The results of this study are expected to contribute to annual cost savings, with a reduction of USD 217,000 in electricity purchasing costs and USD 19,700 in greenhouse gas emissions trading expenses. [ABSTRACT FROM AUTHOR]

Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer's demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting. [ABSTRACT FROM AUTHOR]

The RAST-K v2, a novel nodal diffusion code, was developed at the Ulsan National Institute of Science and Technology (UNIST) for designing the cores of pressurized water reactors (PWR) and performing analyses with high accuracy and computational performance by adopting state-of-the-art calculation models and various engineering features. It is a three-dimensional multi-group nodal diffusion code developed for the steady and transient states using microscopic cross-sections generated by the STREAM code for 37 isotopes. A depletion chain containing 22 actinides and 15 fission products and burnable absorbers was solved using the Chebyshev rational approximation method. A simplified one-dimensional single-channel thermal-hydraulic calculation was performed with various values for the thermal conductivity. Advanced features such as burnup adaptation and CRUD modeling capabilities are implemented for the multi-cycle analysis of commercial reactor power plants. The performance of RAST-K v2 has been validated with the measured data of PWRs operating in Korea. Furthermore, RAST-K v2 has been coupled with a sub-channel code (CTF), fuel performance code (FRAPCON), and water chemistry code for multiphysics analyses. In this paper, the calculation models and engineering features implemented in RAST-K v2 are described, and then the application status of RAST-K v2 is presented. [ABSTRACT FROM AUTHOR]

Solar irradiance models contribute to mitigating the lack of measurement data at a ground station. Conventionally, the models relied on physical calculations or empirical correlations. Recently, machine learning as a sophisticated statistical method has gained popularity due to its accuracy and potential. While some studies compared machine learning models with other models, a study has not yet been performed that compares them side-by-side to assess their performance using the same datasets in different locations. Therefore, this study aims to evaluate the accuracy of three representative models for estimating solar irradiance using atmospheric variables measurement and cloud amount derived from satellite images as the input parameters. Based on its applicability and performance, this study selected the fast all-sky radiation model for solar applications (FARMS) derived from the radiative transfer approach, the Hammer model that simplified atmospheric correlation, and the long short-term memory (LSTM) model specialized in sequential datasets. Global horizontal irradiance (GHI) data were modeled for five distinct locations in South Korea and compared with hourly measurement data of two years to yield the error metrics. When identical input parameters were used, LSTM outperformed the FARMS and the Hammer model in terms of relative root mean square difference (rRMSD) and relative mean bias difference (rMBD). Training an LSTM model using the input parameters of FARMS, such as ozone, nitrogen, and precipitable water, yielded more accurate results than using the Hammer model. The result shows unbiased and accurate estimation with an rRMSD and rMBD of 23.72% and 0.14%, respectively. Conversely, the FARMS has a faster processing speed and does not require significant data to make a fair estimation. [ABSTRACT FROM AUTHOR]

The Agrophotovoltaic (APV) system is a novel concept in the field of Renewable Energy Systems. This system enables the generation of solar energy via photo-voltaic (PV) modules above crops, to mitigate harmful impact on food production. This study aims to develop a performance evaluation model for an APV system in a temperate climate region, such as South Korea. To this end, both traditional electricity generation models (solar radiation-based model and climate-based model) of PV modules and two major machine learning (ML) techniques (i.e., polynomial regression and deep learning) have been considered. Electricity generation data was collected via remote sensors installed in the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. Moreover, economic analysis in terms of cost and benefit of the subject APV system was conducted to provide information about the return on investment to farmers and government agencies. As a result, farmers, agronomists, and agricultural engineers can easily estimate performance and profit of their APV systems via the proposed performance model. [ABSTRACT FROM AUTHOR]

The ongoing upsurge of deep learning and artificial intelligence methodologies manifest incredible accomplishment in a broad scope of assessing issues in different industries, including the energy sector. In this article, we have presented a hybrid energy forecasting model based on machine learning techniques. It is based on the three machine learning algorithms: extreme gradient boosting, categorical boosting, and random forest method. Usually, machine learning algorithms focus on fine-tuning the hyperparameters, but our proposed hybrid algorithm focuses on the preprocessing using feature engineering to improve forecasting. We also focus on the way to impute a significant data gap and its effect on predicting. The forecasting exactness of the proposed model is evaluated using the regression score, and it depicts that the proposed model, with an R-squared of 0.9212, is more accurate than existing models. For the testing purpose of the proposed energy consumption forecasting model, we have used the actual dataset of South Korea's hourly energy consumption. The proposed model can be used for any other dataset as well. This research result will provide a scientific premise for the strategy modification of energy supply and demand. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) is very important for planning and operating power systems and markets. Various algorithms have been developed for STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based on reinforcement learning is proposed to substitute the dependence on an expert's experience. The proposed algorithm consists of the selection of similar days, which is based on the reinforcement algorithm, and the STLF, which is based on an artificial neural network. The proposed similar day selection model based on the reinforcement learning algorithm is developed based on the Deep Q-Network technique, which is a value-based reinforcement learning algorithm. The proposed similar day selection model and load forecasting model are tested using the measured load and meteorological data for Korea. The proposed algorithm shows an improvement accuracy of load forecasting over previous algorithms. The proposed STLF algorithm is expected to improve the predictive accuracy of STLF because it can be applied in a complementary manner along with other load forecasting algorithms. [ABSTRACT FROM AUTHOR]

Finding optimal panel tilt angle of photovoltaic system is an important matter as it would convert the amount of sunlight received into energy efficiently. Numbers of studies used various research methods to find tilt angle that maximizes the amount of radiation received by the solar panel. However, recent studies have found that conversion efficiency is not solely dependent on the amount of radiation received. In this study, we propose a solar panel tilt angle optimization model using machine learning algorithms. Rather than trying to maximize the received radiation, the objective is to find tilt angle that maximizes the converted energy of photovoltaic (PV) systems. Considering various factors such as weather, dust level, and aerosol level, five forecasting models were constructed using linear regression (LR), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine (SVM), and gradient boosting (GB). Using the best forecasting model, our model showed increase in PV output compared with optimal angle models. [ABSTRACT FROM AUTHOR]