건물 예측 제어용 LSTM 기반 일사 예측 모델
Development of a Prediction Model of Solar Irradiances Using LSTM for use in Building Predictive Control
1
조회
0
다운로드
5
인용
0
참고문헌
키워드
초록
The purpose of the work is to develop a simple solar irradiance prediction model using a deep learning method, the LSTM (long term short term memory). Other than existing prediction models, the proposed one uses only the cloudiness among the information forecasted from the national meterological forecast center. The future cloudiness is generally announced with four categories and for three-hour intervals. In this work, a daily irradiance pattern is used as an input vector to the LSTM together with that cloudiness information. The proposed model showed an error of 5% for learning and 30% for prediction. This level of error has lower influence on the load prediction in typical building cases.
Abstract
The purpose of the work is to develop a simple solar irradiance prediction model using a deep learning method, the LSTM (long term short term memory). Other than existing prediction models, the proposed one uses only the cloudiness among the information forecasted from the national meterological forecast center. The future cloudiness is generally announced with four categories and for three-hour intervals. In this work, a daily irradiance pattern is used as an input vector to the LSTM together with that cloudiness information. The proposed model showed an error of 5% for learning and 30% for prediction. This level of error has lower influence on the load prediction in typical building cases.
참고문헌 (33)
- Zhuang, J., "New Simplified Modeling Method for Model Predictive Control in a Medium-Sized Commercial Building: A Case Study", Build. Environ, 127, pp.1~12 (2018)
- Henze, G. P., "Evaluation of Optimal Control for Active and Passive Building Thermal Storage", International Journal of Thermal Sciences, 43, pp.173~183 (2004)
- Ferreira, P. M., "Neural Networks Based Predictive Control for Thermal Comfort and Energy Savings in Public Buildings", Energy and Buildings, 55, pp.238~251 (2012)
- Huang, H, "A New Model Predictive Control Scheme for Energy and Cost Savings in Commercial Buildings: An Airport Terminal Building Case Study", Building and Environment, 89, pp.203~216
- Kusiak, A., "Modeling and Optimization of HVAC Energy Consumption", Applied Energy, 87(10), pp.3092~3102 (2010)
- Nguyen, T. T., "Analyzing the Impacts of System Parameters on MPC-based Frequency Control for a Stand-alone Microgrid", Energies, 10(4), pp.417~ (2017)
- Afram, A., "Theory and Applications of HVAC Control Systems-A Review of Model Predictive Control (MPC)", Building and Environment, 72, pp.343~355
- Khanmirza, E., "Predictive Control of a Building Hybrid Heating System for Energy Cost Reduction", Applied Soft Computing, 46, pp.407~423 (2016)
- Jeon, B. K., "Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems", Sustainability, 11(1), pp.1~16 (2018)
- Premalatha, N., "Prediction of Solar Radiation for Solar Systems by Using ANN Models with Different Back Propagation Algorithms", Journal of Applied Research and Technology, 14(3), pp.206~214 (2016)
- Black, J. N., "The Distribution of Solar Radiation Over the Earth’s Surface", Archiv für Meteorologie, Geophysik und Bioklimatologie, Serie B, 7(2), pp.165~189 (1956)
- Samimi, J., "Estimation of Height-dependent Solar Irradiation and Application to the Solar Climate of Iran", Solar Energy, 52(5), pp.401~409 (1994)
- Paltridge, G. W., "Monthly Mean Solar Radiation Statistics for Australia", Solar Energy, 18(3), pp.235~243 (1976)
- Daneshyar, M., "Solar Radiation Statistics for Iran", Sol. Energy;(United States), 21(4) (1978)
- Premalatha, N., "Prediction of Solar Radiation for Solar Systems by Using ANN Models with Different Back Propagation Algorithms", Journal of Applied Research and Technology, 14(3), pp.206~214 (2016)
- Lago, J., "Forecasting Spot Electricity Prices: Deep Learning Approaches and Empirical Comparison of Traditional Algorithms", Applied Energy, 221, pp.386~405 (2018)
- Jiang, Y., "Computation of Monthly Mean Daily Global Solar Radiation in China Using Artificial Neural Networks and Comparison with Other Empirical Models", Energy, 34(9), pp.1276~1283 (2009)
- Ahmad, A., "Hourly global solar irradiation forecasting for New Zealand", Solar Energy, 122, pp.1398~1408 (2015)
- Solmaz, O., "Prediction of Daily Solar Radiation Using ANNs for Selected Provinces in Turkey", International Scientific Conference, pp.450~456 (2010)
- Qing, X., "Hourly day-ahead Solar Irradiance Prediction Using Weather Forecasts by LSTM", Energy, 148, pp.461~468 (2018)
- "Korea Official Meteorological Agency"[URL]
- "UK Official Meteorological Agency"[URL]
- "United States Official Meteorological Agency"[URL]
- "Canadian Official Meteorological Agency"[URL]
- "The Korean Solar Energy Society"[URL]
- Lee, L. J., "A Study on Fundamental and Application of CNN and RNN", Broadcasting and Media Magazine, 22(1), pp.87~95 (2017)
- Saito, G., "Deep Learning from Scratch" (2017), Hanbit Media Inc
- Baccouche, M., "Sequential Deep Learning for Human Action Recognition", International Workshop on Human Behavior Understanding, pp.29~39 (2011), Springer
- Kinga, D., "A Method for Stochastic Optimization", International Conference on Learning Representations(ICLR), 5 (2015)
- 공동석, "도심지역 에너지계획을 위한 인공신경망 기반의 에너지수요예측에 관한 연구", 대한건축학회논문집, 26(02), pp.221~230 (2010), 대한건축학회
- "Documentation, M.", The MathWorks Inc, ~ (2005)
- Gutierrez-Corea, F. V., "Forecasting Short-term Solar Irradiance Based on Artificial Neural Networks and Data from Neighboring Meteorological Stations", Solar Energy, 134, pp.119~131 (2016)
- Kim, T. H., "A Study on Solar Radiation Model for Prediction of Solar Insolation", pp.670~675 (2013), The Korean Society of Mechanical Engineers