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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]

• This work focuses on the PV power output forecasting using time series algorithms. • The input datasets are selected PV data from north region of South Korea. • Feature selection for time series algorithms is based on different seasons and climate changes. • PV power generation forecasting by different AI models has carried out on an hourly basis to test efficacy. • LSTM model shows the lowest error rate for quick PV power generation forecasting. The present photovoltaic (PV) power generation systems are globally facing the irregularity problem in the distribution of PV generation. In particular, the exact PV power forecasting is critical for grid-connected photovoltaic (PV) systems under unwanted changes in environmental circumstances. The grid energy management, grid operation and scheduling are important factors to forecast the PV power output. Time series analysis is one of the most important aspects of PV output prediction, especially in places (in South Korea) where past solar radiation data or other weather parameters have not been recorded. In this paper, a variety of time-series methods including deep-learning algorithm and machine learning algorithms was used to predict the PV power generation output for quick respond to equipment and panel defects. For designing AI models, the input data were characterized by dividing seasons and choosing the multiple parameters from seasons. In this study, the photovoltaic power generation data was collected from Ansan city, South Korea during January 2017 to June 2021 and the weather data was collected from Suwon city, South Korea during January 2017 to June 2021. In this work, approx. 40,000 hours of operation data from 1.5 MW grid-connected PV system in South Korea was used. PV power generation forecasting was carried out on an hourly basis to test efficacy of various models. Among all models (Holt-Winters, Multivariate Linear Regression, ARIMA, SARIMA, ARIMAX, SARIMAX), LSTM model presented the lowest error rate as compared to other models for quick PV power generation forecasting. [ABSTRACT FROM AUTHOR]

Worldwide COVID-19 infections have caused various problems throughout different countries. In the case of Korea, problems related to the demand for medical care concerning wards and doctors are serious, which were already slowly worsening problems in Korea before the COVID-19 pandemic. In this paper, we propose the direction of developing a system by combining artificial intelligence technology with limited areas that do not require high expertise in the rehabilitation medical field that should be improved in Korea through the prevention of bedsores and leg rehabilitation methods. Regarding the introduction of artificial intelligence technology, medical and related laws and regulations were quite limited, so the actual needs of domestic rehabilitation doctors and advice on the hospital environment were obtained. Satisfaction with the test content was high, the degree of provision of important medical data was 95%, and the angular error was within 5 degrees and suitable for recovery confirmation. [ABSTRACT FROM AUTHOR]