Spatial mapping of short-term solar radiation prediction incorporating geostationary satellite images coupled with deep convolutional LSTM networks for South Korea

A practical approach to continuously monitor and provide real-time solar energy prediction can help support reliable renewable energy supply and relevant energy security systems. In this study on the Korean Peninsula, contemporaneous solar radiation images obtained from the Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) system, were used to design a convolutional neural network and a long short-term memory network predictive model, ConvLSTM. This model was applied to predict one-hour ahead solar radiation and spatially map solar energy potential. The newly designed ConvLSTM model enabled reliable prediction of solar radiation, incorporating spatial changes in atmospheric conditions and capturing the temporal sequence-to-sequence variations that are likely to influence solar driven power supply and its overall stability. Results showed that the proposed ConvLSTM model successfully captured cloud-induced variations in ground level solar radiation when compared with reference images from a physical model. A comparison with ground pyranometer measurements indicated that the short-term prediction of global solar radiation by the proposed ConvLSTM had the highest accuracy [root mean square error (RMSE) = 83.458 Wcenterdotm−2, mean bias error (MBE) = 4.466 Wcenterdotm−2, coefficient of determination (R2) = 0.874] when compared with results of conventional artificial neural network (ANN) [RMSE = 94.085 Wcenterdotm−2, MBE = −6.039 Wcenterdotm−2, R2 = 0.821] and random forest (RF) [RMSE = 95.262 Wcenterdotm−2, MBE = −11.576 Wcenterdotm−2, R2 = 0.839] models. In addition, ConvLSTM better captured the temporal variations in predicted solar radiation, mainly due to cloud attenuation effects when compared with two selected ground stations. The study showed that contemporaneous satellite images over short-term or near real-time intervals can successfully support solar energy exploration in areas without continuous environmental monitoring systems, where satellite footprints are available to model and monitor solar energy management systems supporting real-life power grid systems

Forecasting for concentrated solar thermal power plants in Australia

Up to 50% of electricity needs in Australia could be supplied by solar power. At these high levels of solar power generation, solar forecasting is necessary to manage the impact of solar variability. However, there has been little research on using solar forecasting in Australia. This study used modelling to investigate the benefits of using short-term and long-term solar forecasts to operate a concentrated solar thermal (CST) plant for a year at four sites that covered different climate zones within the Australian National Electricity Market. Using 1-hour ahead short-term forecasts increased net value by $0.90-$2.07 million for a CST plant with storage, and by $0.76-$3.10 million for a CST plant without storage. It also improved reliability by reducing the equivalent forced outage rate by 21-38 percentage points for a CST plant with storage, and by 16-42 percentage points for a CST plant without storage. Using 1-hour forecasts achieved 59%-94% of the net value achievable if the 48-hour forecast were perfect. At each site, the highest net value and reliability were achieved by a CST plant with storage and using 1-hour forecasts, thus a CST plant should have both storage and short-term forecasts. If only one can be used, then a CST plant with storage and without 1-hour forecasts achieves higher net value, whereas a CST plant without storage and with 1-hour forecasts achieves higher reliability. These results demonstrated that using short-term forecasts is beneficial for CST plants that operate in electricity markets that allow updated bids to be submitted at short-term time frames. The results can be used to estimate the return on investment in obtaining short-term forecasts for operating a CST plant. Furthermore, the research method can be adapted into a tool for estimating value to assist CST plant project planning

Calculation of Solar Irradiance from Weather Station Data and Satellite Images by Regionally Training Artificial Neural Network Models

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 에너지시스템공학부, 2019. 2. 박형동.There are primarily two ways to estimate the solar irradiance of a certain area. One method is to directly measure the solar irradiance with a pyranometer, and the other is to indirectly estimate solar irradiance by satellite images. The former is good at accurately measuring solar irradiance with constant time intervals, but has a disadvantage in that the extent of measurement is limited to a small area. In contrast, although the latter can provide a wide range of estimation, the accuracy of its real time estimation is not so reliable. In order to complement the shortcomings of these two methods, this study uses COMS satellite images and Automated Synoptic Observing System (ASOS) weather observation data of the KMA (Korea Meteorological Administration). The interactions of multiple variables from these data sources with solar irradiance are so complex that it is difficult to build physical models to completely explain these mechanisms. Therefore, artificial neural network (ANN) is used for estimation, and the accuracy of ANN models are improved by training data regionally and applying average ensemble models. In order to train data for each region, solar irradiance regions need to be determined. Cloud indices (CI) were calculated from the visible channel of a COMS geostationary satellite, and its dimension was reduced by principal component analysis (PCA). Then, the data were classified into several regions by applying K-means clustering, and the optimal number of clusters was determined by L-method using the CH index. As a result, Korea was divided into 13 irradiance regions, 12 of which are practically used to build models, with the exception of one region of the sea. Ten input variables for the training of an artificial neural network were selected from 14 variablesthe 14 variables are composed of three variables of solar geometry, five variables from KMA ASOS stations, and six variables from COMS satellite images. A feature selection process was conducted by the mutual information feature selection method. From 10 selected variables, the multi-layer perceptron networks were built in MATLAB software environment. Data from 2016 were used for a training dataset, and data from 2017 were used for a validation dataset. The result of cross validation showed 0.96 of correlation coefficient 80.87 W/㎡ of RMSE and 22.5% of rRMSE. In particular, a clear day showed a higher accuracy with 0.98 of correlation coefficient and 15% of rRMSE. However, a cloudy and overcast day showed lower accuracy. After ANN models were built by training a dataset from 2016, the model was applied to the dataset from 2017 for validation. Since the validation results showed similar accuracy with training, the model could be applied to any time series data. In addition, July to August showed lower accuracy because it is the rainy season in Korea. The regionally trained ANN ensemble models showed better accuracy than globally trained models, and also better than single models. Finally, the solar irradiances at 94 KMA ASOS stations were calculated from these models, so the annual and monthly solar irradiances of these sites in 2017 were calculated. The results of the calculation of annual solar irradiance showed that the irradiance is highly related to the irradiance regions. The monthly solar irradiance increased from January to May and June, and it decreased dramatically in the rainy season – July and August. These regional solar irradiance models can make it possible to estimate the solar irradiance of points that have no measured data from a pyranometer. These models can be applied to anywhere meteorological data is observed. Therefore, this can contribute to calculating hourly, daily, monthly, and annual solar irradiance more densely and accurately than existing solar irradiance measuring networks.어느 지역의 일사량을 파악하는 데는 두 가지 방법을 사용할 수 있다. 첫 번째는 일사계를 이용하여 일사량을 직접 측정하는 방법이고, 다른 방법은 위성영상을 이용하여 일사량 값을 간접적으로 추정하는 방법이다. 일사계를 이용하면 시간별로 정확한 일사량 값을 알 수 있지만, 시스템 구축에 많은 비용이 들고 일사량을 파악할 수 있는 범위가 제한적이라는 단점이 있다. 반면 위성영상을 이용하는 방법은 넓은 영역에 걸친 일사량을 알 수 있다는 장점이 있지만, 실시간 일사량 산출의 정확도가 높지 않다는 단점이 있다. 본 연구에서는 이 두 가지 방법의 단점을 보완하기 위해 천리안 기상위성 영상과 기상청의 종관기상관측 자료를 이용하여 일사량을 추정하였다. 이때, 일사량에 영향을 미치는 변수들 간의 물리식을 알기 어렵기 때문에 인공신경망 방법을 활용하였으며, 권역별로 인공신경망을 훈련시키고 여러 개의 인공신경망 모델 결과를 평균한 앙상블 모델을 활용함으로써 정확도를 향상시켰다. 인공신경망 훈련에 앞서서 천리안 기상위성의 가시광 채널 영상으로부터 일별 구름지수를 산출한 뒤, 주성분 분석을 통해 차원을 축소하고 K 평균 군집화를 적용함으로써 일사량 권역을 분류하였다. 권역의 개수는 분류가 잘 되었는지 평가하는 CH 지수를 활용한 L-method 방법을 통해 결정했으며, 이를 통해 한국을 총 13개의 권역, 바다를 제외하면 12개의 권역으로 분류하였다. 인공신경망 훈련을 위한 입력데이터로는, 태양의 위치를 나타내는 3개의 변수, 기상관측소 자료의 5개의 변수, 천리안 영상을 통해 얻은 6개의 변수 총 14개의 변수들 중에서 상호정보량 특징 추출을 이용하여 10개의 변수를 선택하였다. 선택된 10개의 변수를 가지고 MATLAB 소프트웨어를 활용하여 다중계층신경망을 구축했으며, 2016년 자료를 훈련자료로, 2017년 자료를 검증자료로 설정하였다. 38개 일사량 관측 지점에 대해 교차검증을 수행한 결과 상관계수 0.96, RMSE 80.87 W/㎡, rRMSE 22.6% 정도의 정확도를 기대할 수 있었으며, 특히 맑은 날에는 상관계수 0.98, rRMSE 15% 정도의 높은 정확도를 보였다. 하지만 구름의 양이 많아질수록 정확도가 감소했다. 2016년 자료로 훈련한 모델을 2017년 자료에 적용하여 검증을 수행한 결과, 훈련의 정확도와 검증의 정확도가 비슷하게 나타나 모델의 시간적 보편성을 확인할 수 있었다. 본 연구에서 적용한 권역별 훈련 모델은 권역별로 훈련하지 않은 전국적 모델에 비해 오차를 2% 정도 감소시킬 수 있었으며, 앙상블 기반의 인공신경망 모델 역시 단일 모델보다 오차를 2% 정도 감소시킴으로써 정확도를 향상시키는 효과가 있었다. 이렇게 구축한 인공신경망 모델을 적용하여 94개의 기상청 종관기상관측 지점에 대해 2017년 한 해 동안의 시간별 일사량을 산출하였고, 이를 통해 각 지점의 연평균 일사량과 월평균 일사량도 산출하였다. 월평균 일사량 산출결과를 살펴보면 일반적으로 1월부터 5, 6월까지 일사량이 점차 증가하다가 7, 8월에 급격히 감소하는 경향을 보였는데, 이는 여름철에는 장마와 태풍 등의 영향으로 일사량이 감소하기 때문으로 보인다. 본 연구의 권역별 일사량 모델을 적용하면 일사량 관측값은 없더라도 실시간으로 기상자료 획득이 가능한 지점에 대해 실시간 일사량을 추정할 수 있다. 이를 통해 기존의 일사량 관측망보다 정밀하고 정확하게 일사량을 추정할 수 있을 것으로 기대된다.1. 서론 1 1.1. 연구배경 1 1.2. 태양복사와 일사량 4 1.2.1. 태양 복사 스펙트럼 4 1.2.2. 대기에 의한 태양복사의 반사, 흡수, 산란 5 1.3. 인공신경망의 원리와 응용 8 1.3.1. 인공신경망의 개념 8 1.3.2. 다중계층신경망 11 2. 연구지역 및 데이터 14 2.1. 기상관측자료 14 2.2. 위성영상 자료 14 3. 권역별 인공신경망 훈련 17 3.1. 일사량 권역분류 17 3.1.1. 구름지수 산출 17 3.1.2. 주성분 분석을 통한 차원 축소 21 3.1.3. K-평균 군집화 23 3.1.4. K-평균 군집화의 최적군집 개수 설정 23 3.1.5. 권역별 분류 결과 26 3.2. 입력데이터 선택 30 3.2.1. 상호 정보량 특징 추출 30 3.2.2. 변수 선택 결과 33 3.3. 인공신경망 훈련 37 4. 인공신경망 모델의 검증과 비교 40 4.1. 정확도를 나타내는 인자 40 4.2. 인공신경망 모델의 지점별 검증 결과 41 4.3. 인공신경망 모델의 기상조건별 검증 결과 46 4.4. 인공신경망 모델의 연도별 검증 결과 55 4.5. 인공신경망 모델의 월별 검증 결과 59 4.6. 인공신경망 모델의 시간별 검증 결과 60 4.7. 권역별 훈련 모델과 전국적 훈련 모델의 비교 62 4.8. 앙상블 모델과 단일 모델의 비교 66 5. 인공신경망 모델의 적용과 일사량 추정 70 6. 결론 79 참고문헌 81 Abstract 89Maste

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources

Atmospheric effects on land classification using satellites and their correction.

Haze occurs almost every year in Malaysia and is caused by smoke which originates from forest fire in Indonesia. It causes visibility to drop, therefore affecting the data acquired for this area using optical sensor such as that on board Landsat - the remote sensing satellite that have provided the longest continuous record of Earth's surface. The work presented in this thesis is meant to develop a better understanding of atmospheric effects on land classification using satellite data and method of removing them. To do so, the two main atmospheric effects dealt with here are cloud and haze. Detection of cloud and its shadow are carried out using MODIS algorithms due to allowing optimal use of its rich bands. The analysis is applied to Landsat data, in which shows a high agreement with other methods. The thesis then concerns on determining the most suitable classification scheme to be used. Maximum Likelihood (ML) is found to be a preferable classification scheme due to its simplicity, objectivity and ability to classify land covers with acceptable accuracy. The effects of haze are subsequently modelled and simulated as a summation of a weighted signal component and a weighted pure haze component. By doing so, the spectral and statistical properties of the land classes can be systematically investigated, in which showing that haze modifies the class spectral signatures, consequently causing the classification accuracy to decline. Based on the haze model, a method of removing haze from satellite data was developed and tested using both simulated and real datasets. The results show that the removal method is able clean up haze and improve classification accuracy, yet a highly non-uniform haze may hamper its performance

The global tree carrying capacity (keynote)

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