scholarly journals Spatiotemporal Optimization for Short-Term Solar Forecasting Based on Satellite Imagery

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2216
Author(s):  
Myeongchan Oh ◽  
Chang Ki Kim ◽  
Boyoung Kim ◽  
Changyeol Yun ◽  
Yong-Heack Kang ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Spatial Data ◽  
Motion Vector ◽  
Satellite Image ◽  
Skill Score ◽  
Solar Forecasting ◽  
Short Term ◽  
Forecast Horizon ◽  
Forecasting Models ◽  
Optimized Model

Solar forecasting is essential for optimizing the integration of solar photovoltaic energy into a power grid. This study presents solar forecasting models based on satellite imagery. The cloud motion vector (CMV) model is the most popular satellite-image-based solar forecasting model. However, it assumes constant cloud states, and its accuracy is, thus, influenced by changes in local weather characteristics. To overcome this limitation, satellite images are used to provide spatial data for a new spatiotemporal optimized model for solar forecasting. Four satellite-image-based solar forecasting models (a persistence model, CMV, and two proposed models that use clear-sky index change) are evaluated. The error distributions of the models and their spatial characteristics over the test area are analyzed. All models exhibited different performances according to the forecast horizon and location. Spatiotemporal optimization of the best model is then conducted using best-model maps, and our results show that the skill score of the optimized model is 21% better than the previous CMV model. It is, thus, considered to be appropriate for use in short-term forecasting over large areas. The results of this study are expected to promote the use of spatial data in solar forecasting models, which could improve their accuracy and provide various insights for the planning and operation of photovoltaic plants.

scholarly journals Very Short-Term Surface Solar Irradiance Forecasting Based On FengYun-4 Geostationary Satellite

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2606 ◽  
Author(s):  
Liwei Yang ◽  
Xiaoqing Gao ◽  
Jiajia Hua ◽  
Pingping Wu ◽  
Zhenchao Li ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Near Infrared ◽  
Motion Vector ◽  
Skill Score ◽  
Geostationary Satellite ◽  
Short Term ◽  
Direct Normal Irradiance ◽  
Skill Scores ◽  
Meteorological Observatory ◽  
Using Data

An algorithm to forecast very short-term (30–180 min) surface solar irradiance using visible and near infrared channels (AGRI) onboard the FengYun-4A (FY-4A) geostationary satellite was constructed and evaluated in this study. The forecasting products include global horizontal irradiance (GHI) and direct normal irradiance (DNI). The forecast results were validated using data from Chengde Meteorological Observatory for four typical months (October 2018, and January, April, and July 2019), representing the four seasons. Particle Image Velocimetry (PIV) was employed to calculate the cloud motion vector (CMV) field from the satellite images. The forecast results were compared with the smart persistence (SP) model. A seasonal study showed that July and April forecasting is more difficult than during October and January. For GHI forecasting, the algorithm outperformed the SP model for all forecasting horizons and all seasons, with the best result being produced in October; the skill score was greater than 20%. For DNI, the algorithm outperformed the SP model in July and October, with skill scores of about 12% and 11%, respectively. Annual performances were evaluated; the results show that the normalized root mean square error (nRMSE) value of GHI for 30–180 min horizon ranged from 26.78% to 36.84%, the skill score reached a maximum of 20.44% at the 30-min horizon, and the skill scores were all above 0 for all time horizons. For DNI, the maximum skill score was 6.62% at the 180-min horizon. Overall, compared with the SP model, the proposed algorithm is more accurate and reliable for GHI forecasting and slightly better for DNI forecasting.

scholarly journals The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Abdelhakim El hendouzi ◽  
Abdennaser Bourouhou ◽  
Omar Ansari
Keyword(s):  
Absolute Error ◽  
Skill Score ◽  
Weather Data ◽  
Short Term ◽  
Pv System ◽  
Pv Systems ◽  
Forecasting Models ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Power Forecasting

The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.

scholarly journals Penggunaan Citra Satelit untuk Mengetahui Persebaran Dacrycarpus imbricatus (Blume) de Laub. di Bukit Tapak, Cagar Alam Batukahu Bali

2017 ◽  
Vol 11 (2) ◽  
pp. 130
Author(s):  
Rajif Iryadi ◽  
Arief Priyadi ◽  
I Dewa Putu Darma
Keyword(s):  
Satellite Imagery ◽  
Spatial Data ◽  
Satellite Image ◽  
Visual Interpretation ◽  
Site Characteristics ◽  
Natural Reserve ◽  
High Resolution Satellite Imagery ◽  
The One ◽  
Total Accuracy

Citra Pleaides merupakan aset penting untuk memperoleh data dan informasi tentang struktur vegetasi di hutan yang sulit untuk diukur langsung karena wilayah yang tidak dapat diakses dan memiliki cakupan luas. Dacrycarpus imbricatus (Blume) de Laub. merupakan salah satu tanaman khas di Bukit Tapak yang memiliki nilai konservasi dan nilai ekonomi. Penelitian ini bertujuan untuk mengetahui persebaran D. imbricatus menggunakan data citra satelit Pleaides yang memiliki resolusi spasial tinggi. Penelitian dilakukan dengan interpretasi visual pada citra satelit Pleaides tahun 2014 dan data spasial elevasi. Akurasi citra Pleaides dalam identifikasi tutupan D. imbricatus mencapai 96,83% dan ketelitian total pemetaan mencapai 93,38% dengan koefisien kappa 88,64%. Persebaran D. imbricatus di Bukit Tapak memiliki range habitat aktual lebih sempit dibandingkan dengan range potensialnya yakni pada elevasi 1.321-1.800 mdpl dengan persentase tutupan 89,52% dari total tutupannya. Lereng Bukit Tapak dengan kemiringan 25,1-55% memiliki lingkup tutupan D. imbricatus sebesar 79,29% dari total tutupannya dan sisanya pada lereng>25%. Informasi tersebut penting terkait dengan kelestarian dan usaha konservasi salah satu jenis tumbuhan berbiji terbuka ini di Bukit Tapak.Kata kunci: akurasi; interpretasi; kanopi; konservasi; pleaides Usage of Satellite Imagery to Determine Distribution of Dacrycarpus imbricatus (Blume) De Laub. on The Tapak Hill, Batukahu Natural Reserve BaliAbstractPleaides image is an important asset to obtain data and information with regard to the structure of the vegetation in the forest that are difficult to measure directly as the area is inaccessible and has a large coverage. Dacrycarpus imbricatus (Blume) de Laub. is the one of typical plants on the Tapak Hill which has the conservation and economic values. This study aimed to determine the location and distribution of D. imbricatus using Pleaides satellite image that had a high spatial resolution. The determination of site characteristics was conducted by visual interpretation of high resolution satellite imagery Pleiades 2014 and elevation spatial data. Pleaides accuracy in the identification cover of D. imbricatus reached 96.83% and total accuracy mapping reached 93.38% with kappa coefficient of 88.64%. The distribution of D. imbricatus in Tapak Hill showed actual habitat range narrower than of its potential, which was distributed on the elevation of 1,321 – 1,800 m asl with a percentage of 89,52% from its total cover. About 79.29% of the coverage laid on the slope of 25.1 to 55%, whereas the rest on the slope of >25%. This information is important related to sustainability and conservation efforts for this gymnosperm plant in Tapak Hill. 

scholarly journals INTEGRASI DATA INDERAJA DAN DATA GEOLOGI UNTUK MENDUKUNG EKSPLORASI TAMBANG EMAS, Studi Kasus di Kabupaten Paniai Provinsi Papua

2017 ◽  
Vol 19 (1) ◽  
pp. 75 ◽  
Author(s):  
Iksal Yanuarsyah ◽  
Yatin Suwarno
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Spatial Data ◽  
Satellite Image ◽  
Image Interpretation ◽  
Geological Mapping ◽  
Alteration Zone ◽  
Mineralized Zone ◽  
Landsat Satellite ◽  
Mining Exploration

<p align="center"><strong><span style="text-decoration: underline;">ABSTRAK</span></strong></p><p>Pemetaan potensi sumberdaya geologi pertambangan khususnya potensi mineral perlu dilakukan sebagai awal dalam pengelolaan sumberdaya pertambangan terlebih dalam tahapan eksplorasi pendahuluan. Penginderaan jauh (Inderaja) merupakan alat bantu yang merekam rona lingkungan bumi yang mampu menginterpretasi potensi eksplorasi mineral logam seperti emas. Dengan menggunakan data citra satelit, biaya eksplorasi akan lebih rendah, termasuk efisiensi dalam melakukan pemboran. Tujuan dari studi ini yaitu mampu mendeliniasi Jalur Alterasi dengan interpretasi citra satelit agar untuk mendukung kegiatan eksplorasi tambang lebih efektif dan efisien. Lokasi kajian berada di Distrik Bogobaida, Kabupaten Paniai, Propinsi Papua seluas 40.116 Ha yang merupakan lokasi Izin Usaha Pertambangan (IUP) Eksplorasi PT. Kotabara Mitratama (izin berdasarkan Keputusan Bupati Paniai No. 017 Tahun 2010). Metode yang digunakan dalam kajian ini yaitu metode konseptual dengan memanfaatkan faktor geologi yang berpengaruh pada terbentuknya endapan minera). Tahapan analisa dimulai dari pengumpulan data spasial (peta) dan non spasial (tabular), analisa interpretasi citra Landsat dan identifikasi kelurusan zona lemah (lineament) untuk menentukan zona mineralisasi. Berdasarkan hasil interpretasi citra Landsat dengan didukung analisa geologi untuk daerah IUP PT. Kotabara Mitratama berprospek Tembaga (Cu) dan Emas (Au) yang terbagi dalam 9 Zona Mineralisasi dengan luas mencapai 2.922,48 Ha (yang terdiri dari 8 zona mineralisasi primer seluas 2.208,83 Ha dan 1 zona mineralisasi aluvial seluas 713,65 Ha).</p><p> Kata kunci: data inderaja, data geologi, eksplorasi emas</p><p align="center"> </p><p align="center"> <strong><em>ABSTRACT</em></strong></p><p> <em>Geological mapping of the mineral potential has to be done as the preliminary stages of mining exploration. Remote sensing is a common tool that used to records the earth's environment through image interpretation such for gold mine potential exploration. </em><em>By using satellite imagery data, will be lower exploration costs, including efficiency in drilling</em><em> </em><em>The aim of this study is to delineate alteration zone with satellite image interpretation to support mining exploration activities more effectively and efficiently. The study Located in Bogobaida District, Paniai Regency, Papua Province, covering an area of 40 116 hectares, in site case of Legal Mining Exploration Permit (IUP) PT. Kotabara Mitratama (Paniai Regent Decree No. 017 of 2010). The method used is utilizing conceptual geological factors that alleged the formation of mineral deposits. Stages of analysis starting from spatial data (maps) and non-spatial (tabular) collection, then Landsat satellite imagery interpretation and identification of weak zones straightness (lineament) due to define the mineralized zones. Based on the results of image interpretation with geological analysis in IUP PT. Kotabara Mitratama was prospected Copper (Cu) and gold (Au) which is divided into 9 Mineralization Zone with an area of 2,922.48 ha (consisting of 8 primary mineralized zone covering an area of 2,208.83 ha and 1 alluvial mineralized zone measuring 713.65 ha).</em></p><p> </p><p><em>K</em><em>eywords: Remote sensing, geological data, gold exploration</em></p>

Short-term Load Forecasting by Using Improved GEP and Abnormal Load Recognition

2021 ◽  
Vol 21 (4) ◽  
pp. 1-28
Author(s):  
Song Deng ◽  
Fulin Chen ◽  
Xia Dong ◽  
Guangwei Gao ◽  
Xindong Wu
Keyword(s):  
Gene Expression ◽  
Probability Distribution ◽  
Detection Rate ◽  
Cross Validation ◽  
Normal Load ◽  
Gene Expression Programming ◽  
Load Forecasting ◽  
Short Term ◽  
Forecasting Models ◽  
Short Term Load Forecasting

Load forecasting in short term is very important to economic dispatch and safety assessment of power system. Although existing load forecasting in short-term algorithms have reached required forecast accuracy, most of the forecasting models are black boxes and cannot be constructed to display mathematical models. At the same time, because of the abnormal load caused by the failure of the load data collection device, time synchronization, and malicious tampering, the accuracy of the existing load forecasting models is greatly reduced. To address these problems, this article proposes a Short-Term Load Forecasting algorithm by using Improved Gene Expression Programming and Abnormal Load Recognition (STLF-IGEP_ALR). First, the Recognition algorithm of Abnormal Load based on Probability Distribution and Cross Validation is proposed. By analyzing the probability distribution of rows and columns in load data, and using the probability distribution of rows and columns for cross-validation, misjudgment of normal load in abnormal load data can be better solved. Second, by designing strategies for adaptive generation of population parameters, individual evolution of populations and dynamic adjustment of genetic operation probability, an Improved Gene Expression Programming based on Evolutionary Parameter Optimization is proposed. Finally, the experimental results on two real load datasets and one open load dataset show that compared with the existing abnormal data detection algorithms, the algorithm proposed in this article have higher advantages in missing detection rate, false detection rate and precision rate, and STLF-IGEP_ALR is superior to other short-term load forecasting algorithms in terms of the convergence speed, MAE, MAPE, RSME, and R 2 .

Review of onsite temperature and solar forecasting models to enable better building design and operations

2021 ◽  
Vol 14 (4) ◽  
pp. 885-907
Author(s):  
Bing Dong ◽  
Reisa Widjaja ◽  
Wenbo Wu ◽  
Zhi Zhou
Keyword(s):  
Building Design ◽  
Solar Forecasting ◽  
Forecasting Models

scholarly journals Multi-Scale Spatiotemporal Change Characteristics Analysis of High-Frequency Disturbance Forest Ecosystem Based on Improved Spatiotemporal Cube Model

2021 ◽  
Vol 13 (13) ◽  
pp. 2537
Author(s):  
Yangcen Zhang ◽  
Xiangnan Liu ◽  
Meiling Liu ◽  
Xinyu Zou ◽  
Qian Zhang ◽  
...  
Keyword(s):  
High Frequency ◽  
Forest Ecosystems ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Anthropogenic Factors ◽  
Residential Areas ◽  
Short Term ◽  
Spatiotemporal Changes ◽  
Spatiotemporal Scales ◽  
The Impact

High-frequency disturbance forest ecosystems undergo complex and frequent changes at various spatiotemporal scales owing to natural and anthropogenic factors. Effectively capturing the characteristics of these spatiotemporal changes from satellite image time series is a powerful and practical means for determining their causes and predicting their trends. Herein, we combined the spatiotemporal cube and vegetation indices to develop the improved spatiotemporal cube (IST-cube) model. We used this to acquire the spatiotemporal dynamics of forest ecosystems from 1987 to 2020 in the study area and then classified it into four spatiotemporal scales. The results showed that the cube-core only exists in the increasing IST-cubes, which are distributed in residential areas and forests. The length of the IST-cube implies the duration of triggers. Human activities result in long-term small-scope IST-cubes, and the impact in the vicinity of residential areas is increasing while there is no change within. Meteorological disasters cause short-term, large scope, and irregular impacts. Land use type change causes short-term small scope IST-cubes and a regular impact. Overall, we report the robustness and strength of the IST-cube model in capturing spatiotemporal changes in forest ecosystems, providing a novel method to examine complex changes in forest ecosystems via remote sensing.

Resolving pre-collapse slope motion at the February 2021 Chamoli rock-ice avalanche via feature tracking of optical satellite imagery

2021 ◽  
Author(s):  
Maximillian Van Wyk de Vries ◽  
Shashank Bhushan ◽  
David Shean ◽  
Etienne Berthier ◽  
César Deschamps-Berger ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Feature Tracking ◽  
Satellite Image ◽  
Image Feature ◽  
Sampling Distance ◽  
Velocity Changes ◽  
The Stability ◽  
Slide Area ◽  
Ice Avalanche

&lt;p&gt;On the 7&lt;sup&gt;th&lt;/sup&gt; of February 2021, a large rock-ice avalanche triggered a debris flow in Chamoli district, Uttarakhand, India, resulting in over 200 dead or missing and widespread infrastructure damage. The rock-ice avalanche originated from a steep, glacierized north-facing slope with a history of instability, most recently a 2016 ice avalanche. In this work, we assess whether the slope exhibited any precursory displacement prior to collapse. We evaluate monthly slope motion over the 2015 and 2021 period through feature tracking of high-resolution optical satellite imagery from Sentinel-2 (10 m Ground Sampling Distance) and PlanetScope (3-4 m Ground Sampling Distance). Assessing slope displacement of the underlying rock is complicated by the presence of glaciers over a portion of the collapse area, which display surface displacements due to internal ice deformation. We overcome this through tracking the motion over ice-free portions of the slide area, and evaluating the spatial pattern of velocity changes in glaciated areas. Preliminary results show that the rock-ice avalanche bloc slipped over 10 m in the 5 years prior to collapse, with particularly rapid slip occurring in the summer of 2017 and 2018. These results provide insight into the precursory conditions of the deadly rock-ice avalanche, and highlight the potential of high-resolution optical satellite image feature tracking for monitoring the stability of high-risk slopes.&lt;/p&gt;

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