• Title/Summary/Keyword: Power generation prediction

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Design of Generation Efficiency Fuzzy Prediction Model using Solar Power Element Data (태양광발전요소 데이터를 활용한 발전효율 퍼지 예측 모델 설계)

  • Cha, Wang-Cheol;Park, Joung-Ho;Cho, Uk-Rae;Kim, Jae-Chul
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.63 no.10
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    • pp.1423-1427
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    • 2014
  • Quantity of the solar power generation is heavily influenced by weather. In other words, due to difference in insolation, different quantity may be generated. However, it does not mean all areas with identical insolation produces same quantity because of various environmental aspects. Additionally, geographic factors such as altitude, height of plant may have an impact on the quantity. Hence, through this research, we designed a system to predict efficiency of the solar power generation system by applying insolation, weather factor such as duration of sunshine, cloudiness parameter and location. By applying insolation, weather data that are collected from various places, we established a system that fits with our nation. Apart from, we produced a geographic model equation through utilizing generated data installed nationwide. To design a prediction model that integrates two factors, we apply fuzzy algorithm, and validate the performance of system by establishing simulation system.

Prediction of Solar Photovoltaic Power Generation by Weather Using LSTM

  • Lee, Saem-Mi;Cho, Kyu-Cheol
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.8
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    • pp.23-30
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    • 2022
  • Deep learning analyzes data to discover a series of rules and anticipates the future, helping us in various ways in our lives. For example, prediction of stock prices and agricultural prices. In this research, the results of solar photovoltaic power generation accompanied by weather are analyzed through deep learning in situations where the importance of solar energy use increases, and the amount of power generation is predicted. In this research, we propose a model using LSTM(Long Short Term Memory network) that stand out in time series data prediction. And we compare LSTM's performance with CNN(Convolutional Neural Network), which is used to analyze various dimensions of data, including images, and CNN-LSTM, which combines the two models. The performance of the three models was compared by calculating the MSE, RMSE, R-Squared with the actual value of the solar photovoltaic power generation performance and the predicted value. As a result, it was found that the performance of the LSTM model was the best. Therefor, this research proposes predicting solar photovoltaic power generation using LSTM.

On the Use of Maximum Likelihood and Input Data Similarity to Obtain Prediction Intervals for Forecasts of Photovoltaic Power Generation

  • Fonseca Junior, Joao Gari da Silva;Oozeki, Takashi;Ohtake, Hideaki;Takashima, Takumi;Kazuhiko, Ogimoto
    • Journal of Electrical Engineering and Technology
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    • v.10 no.3
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    • pp.1342-1348
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    • 2015
  • The objective of this study is to propose a method to calculate prediction intervals for one-day-ahead hourly forecasts of photovoltaic power generation and to evaluate its performance. One year of data of two systems, representing contrasting examples of forecast’ accuracy, were used. The method is based on the maximum likelihood estimation, the similarity between the input data of future and past forecasts of photovoltaic power, and on an assumption about the distribution of the error of the forecasts. Two assumptions for the forecast error distribution were evaluated, a Laplacian and a Gaussian distribution assumption. The results show that the proposed method models well the photovoltaic power forecast error when the Laplacian distribution is used. For both systems and intervals calculated with 4 confidence levels, the intervals contained the true photovoltaic power generation in the amount near to the expected one.

Performance Prediction Model of Solid Oxide Fuel Cell Stack Using Deep Neural Network Technique (심층 신경망 기법을 이용한 고체 산화물 연료전지 스택의 성능 예측 모델)

  • LEE, JAEYOON;PINEDA, ISRAEL TORRES;GIAP, VAN-TIEN;LEE, DONGKEUN;KIM, YOUNG SANG;AHN, KOOK YOUNG;LEE, YOUNG DUK
    • Transactions of the Korean hydrogen and new energy society
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    • v.31 no.5
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    • pp.436-443
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    • 2020
  • The performance prediction model of a solid oxide fuel cell stack has been developed using deep neural network technique, one of the machine learning methods. The machine learning has been received much interest in various fields, including energy system mo- deling. Using machine learning technique can save time and cost requried in developing an energy system model being compared to the conventional method, that is a combination of a mathematical modeling and an experimental validation. Results reveal that the mean average percent error, root mean square error, and coefficient of determination (R2) range 1.7515, 0.1342, 0.8597, repectively, in maximum. To improve the predictability of the model, the pre-processing is effective and interpolative machine learning and application is more accurate than the extrapolative cases.

Recurrent Neural Network based Prediction System of Agricultural Photovoltaic Power Generation (영농형 태양광 발전소에서 순환신경망 기반 발전량 예측 시스템)

  • Jung, Seol-Ryung;Koh, Jin-Gwang;Lee, Sung-Keun
    • The Journal of the Korea institute of electronic communication sciences
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    • v.17 no.5
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    • pp.825-832
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    • 2022
  • In this paper, we discuss the design and implementation of predictive and diagnostic models for realizing intelligent predictive models by collecting and storing the power output of agricultural photovoltaic power generation systems. Our model predicts the amount of photovoltaic power generation using RNN, LSTM, and GRU models, which are recurrent neural network techniques specialized for time series data, and compares and analyzes each model with different hyperparameters, and evaluates the performance. As a result, the MSE and RMSE indicators of all three models were very close to 0, and the R2 indicator showed performance close to 1. Through this, it can be seen that the proposed prediction model is a suitable model for predicting the amount of photovoltaic power generation, and using this prediction, it was shown that it can be utilized as an intelligent and efficient O&M function in an agricultural photovoltaic system.

Short Term Forecast Model for Solar Power Generation using RNN-LSTM (RNN-LSTM을 이용한 태양광 발전량 단기 예측 모델)

  • Shin, Dong-Ha;Kim, Chang-Bok
    • Journal of Advanced Navigation Technology
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    • v.22 no.3
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    • pp.233-239
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    • 2018
  • Since solar power generation is intermittent depending on weather conditions, it is necessary to predict the accurate generation amount of solar power to improve the efficiency and economical efficiency of solar power generation. This study proposes a short - term deep learning prediction model of solar power generation using meteorological data from Mokpo meteorological agency and generation data of Yeongam solar power plant. The meteorological agency forecasts weather factors such as temperature, precipitation, wind direction, wind speed, humidity, and cloudiness for three days. However, sunshine and solar radiation, the most important meteorological factors for forecasting solar power generation, are not predicted. The proposed model predicts solar radiation and solar radiation using forecast meteorological factors. The power generation was also forecasted by adding the forecasted solar and solar factors to the meteorological factors. The forecasted power generation of the proposed model is that the average RMSE and MAE of DNN are 0.177 and 0.095, and RNN is 0.116 and 0.067. Also, LSTM is the best result of 0.100 and 0.054. It is expected that this study will lead to better prediction results by combining various input.

PV Power Prediction Models for City Energy Management System based on Weather Forecast Information (기상정보를 활용한 도시규모-EMS용 태양광 발전량 예측모델)

  • Eum, Ji-Young;Choi, Hyeong-Jin;Cho, Soo-Hwan
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.3
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    • pp.393-398
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    • 2015
  • City or Community-scale Energy Management System(CEMS) is used to reduce the total energy consumed in the city by arranging the energy resources efficiently at the planning stage and controlling them economically at the operating stage. Of the operational functions of the CEMS, generation forecasting of renewable energy resources is an essential feature for the effective supply scheduling. This is because it can develop daily operating schedules of controllable generators in the city (e.g. diesel turbine, micro-gas turbine, ESS, CHP and so on) in order to minimize the inflow of the external power supply system, considering the amount of power generated by the uncontrollable renewable energy resources. This paper is written to introduce numerical models for photo-voltaic power generation prediction based on the weather forecasting information. Unlike the conventional methods using the average radiation or average utilization rate, the proposed models are developed for CEMS applications using the realtime weather forecast information provided by the National Weather Service.

hydraulic-power generation of electricity plan of multi-Purpose dam in electric Power system (전력계통에서의 다목적댐 수력발전계획)

  • Kim, Seung-Hyo;Ko, Young-Hoan;Hwang, In-Kwang
    • Proceedings of the KIEE Conference
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    • 1999.07c
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    • pp.1248-1252
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    • 1999
  • To provide electricity power of good quality, it is essential to establish generation of electricity plan in electric power system based on accurate power-demand prediction and cope with changes of power-need fluctuating constantly. The role of hydraulic-power generation of electricity in electric power system is of importance because responding to electric power-demand counts or reservoir-type hydraulic-power generation of electricity which is designed for additional load in electric power system. So hydraulic-power generation of electricity must have fast start reserve. But the amount of water, resources of reservoir-type hydraulic-power generation of electricity is restricted and multi-used, so the scheduling of management by exact forecasting the amount of water is critical. That is why efficient hydraulic-power generation of electricity makes a main role on pumping up the utility of energy and water resource. This thesis introduced the example of optimal generation of electricity plan establishment which is used in managing reservoir-type hydraulic-power generation of electricity.

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Development of Comparative Verification System for Reliability Evaluation of Distribution Line Load Prediction Model (배전 선로 부하예측 모델의 신뢰성 평가를 위한 비교 검증 시스템)

  • Lee, Haesung;Lee, Byung-Sung;Moon, Sang-Keun;Kim, Junhyuk;Lee, Hyeseon
    • KEPCO Journal on Electric Power and Energy
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    • v.7 no.1
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    • pp.115-123
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    • 2021
  • Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

Development of Photovoltaic Output Power Prediction System using OR-AND Structured Fuzzy Neural Networks (OR-AND 구조의 퍼지 뉴럴 네트워크를 이용한 태양광 발전 출력 예측 시스템 개발)

  • Kim, Haemaro;Han, Chang-Wook;Lee, Don-Kyu
    • Journal of IKEEE
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    • v.23 no.1
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    • pp.334-337
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    • 2019
  • In response to the increasing demand for energy, research and development of next-generation energy is actively carried out around the world to replace fossil fuels. Among them, the specific gravity of solar power generation systems using infinity and pollution-free solar energy is increasing. However, solar power generation is so different from solar energy that it is difficult to provide stable power and the power production itself depends on the solar energy by region. To solve these problems in this paper, we have collected meteorological data such as actual regional solar irradiance, precipitation, temperature and humidity, and proposed a solar power output prediction system using logic-based fuzzy Neural Network.