- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Forecasting Solar Activity with Computational Intelligence Models
摘要： It is vital to accurately predict solar activity, in order to decrease the plausible damage of electronic equipment in the event of a large high-intensity solar eruption. Recently, we have proposed BELFIS (Brain Emotional Learning-based Fuzzy Inference System) as a tool for the forecasting of chaotic systems. The structure of BELFIS is designed based on the neural structure of fear conditioning. The function of BELFIS is implemented by assigning adaptive networks to the components of the BELFIS structure. This paper especially focuses on performance evaluation of BELFIS as a predictor by forecasting solar cycles 16 to 24. The performance of BELFIS is compared with other computational models used for this purpose, and in particular with adaptive neuro-fuzzy inference system (ANFIS).
关键词： Adaptive Neuro-Fuzzy Inference System,Solar Activity Forecasting,Computational Intelligence Models,Brain Emotional Learning-based Fuzzy Inference System,Solar cycles
更新于2025-09-04 15:30:14
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Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth
摘要： A method for retrieving cloud optical depth (τc) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red–blue ratio (RRBR) method is motivated from the analysis of simulated images of various τc produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red–blue ratio (RBR) of a pixel are identified as the solar zenith angle (θ0), τc, solar pixel angle/scattering angle (?s), and pixel zenith angle/view angle (?z). The effects of these parameters are described and the functions for radiance, Iλ (τc, θ0, ?s, ?z), and RBR(τc, θ0, ?s, ?z) are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τc, where RBR increases with τc up to about τc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured I meas (?s, ?z), in addition to RBRmeas (?s, ?z), to obtain a unique solution for τc. The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003) method for overcast skies. τc values ranged from 0 to 80 with values over 80, being capped and registered as 80. A τc RMSE of 2.5 between the Min et al. (2003) method and the USI are observed. The MWR and USI have an RMSE of 2.2, which is well within the uncertainty of the MWR. The procedure developed here provides a foundation to test and develop other cloud detection algorithms.
关键词： sky imager,cloud optical depth,solar forecasting,radiative transfer model,red–blue ratio
更新于2025-09-04 15:30:14
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Preliminary assessment of two spatio-temporal forecasting technics for hourly satellite-derived irradiance in a complex meteorological context
摘要： This paper examines two spatio-temporal approaches for short-term forecasting of global horizontal irradiance using gridded satellite-derived irradiances as experimental support. The first approach is a spatio-temporal vector autoregressive (STVAR) model combined with a statistical process for optimum selection of input variables. The second is an existing operational cloud motion vector (CMV) model. An evaluation of the predictive performance of these models is presented for a case study area in the Caribbean Islands. This region is characterized by a large diversity of microclimates and land/sea contrasts, creating a challenging solar forecasting context. Using scaled persistence as a reference, we benchmark the performance of the two spatio-temporal models over an extended 220 × 220 km domain, and for three specific, climatically distinct locations within this domain. We also assess the influence of intra-day solar resource variability on model performance. Finally, we present preliminary evidence that a blend of CMV and STVAR forecasts leads to improved accuracy under all conditions.
关键词： Cloud motion vector,Spatio-temporal forecasting,Satellite-derived irradiance,Complex meteorological context
更新于2025-09-04 15:30:14
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[IEEE 2018 53rd International Universities Power Engineering Conference (UPEC) - Glasgow, United Kingdom (2018.9.4-2018.9.7)] 2018 53rd International Universities Power Engineering Conference (UPEC) - Load and PV Generation Forecast Based Cost Optimization for Nanogrids with PV and Battery
摘要： Power system resiliency and robustness became major concerns of the system operators and researchers after the introduction of the smart grid concept. The improvements in the battery storage systems (BSS) and the photovoltaic (PV) systems encourage power systems operators to enable the use of those systems in resiliency and robustness studies. Utilization of those systems not only contributes to the robustness of the power systems but also decrease the operational costs. There are several methods in literature to operate the grid systems with partitions of PV and BSS in the most economical way. Although these methods are straightforward and work fine, they can not guarantee the most economical result on a daily basis. In this paper, deep learning based PV generation and load forecasts are used to improve the results of optimization in terms of economic aspects in nano-grid applications. In the considered system, there are loads, PV generation units, BSS and grid connection. Bi-directional power flow is permitted between the main grid and the nano-grid system. The forecasting methodologies and used optimization algorithms will be explained in this paper.
关键词： demand-side management,smart grids,mathematical programming,recurrent neural networks,forecasting
更新于2025-09-04 15:30:14
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[ACM Press the 24th ACM Symposium - Tokyo, Japan (2018.11.28-2018.12.01)] Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology - VRST '18 - Real-time human motion forecasting using a RGB camera
摘要： We propose a real-time human motion forecasting system which visualize the future pose in virtual reality using a RGB camera. Our system consists of three parts: 2D pose estimation from RGB frames using a residual neural network, 2D pose forecasting using a recurrent neural network, and 3D recovery from the predicted 2D pose using a residual linear network. To improve the prediction learning quantity of temporal feature, we propose a special method using lattice optical flow for the joints movement estimation. After fitting the skeleton, a predicted 3d model of target human will be built 0.5s in advance in a 30-fps video.
关键词： Deep neural network,Real-time pose prediction,Motion forecasting
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) - Palermo (2018.6.12-2018.6.15)] 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) - Outdoor Workers Exposed to UV Radiation: Comparison of UV Index Forecasting Methods
摘要： Ultra-Violet (UV) radiation covers only a minimal part of solar spectrum (5%) but produces severe effects on human health. Everyone is exposed to Sun but some outdoor workers categories are exposed not only in their leisure time but also during their working activities. To protect outdoor workers health, daily estimates of UV radiation are needed and without a national service providing a measured UV Index (UVI) in the Italian territory, UVI forecasting methods are welcomed. Forecasting methods can be classified in two major groups: methods based on the estimation of the UV total irradiance from solar total irradiance and methods based on regression models of measured UVIs. The former have greater uncertainties (about 30%) and are more suitable for epidemiologic studies, the latter, due to their better precision (about 10-20%), are more suitable for daily UVI evaluation.
关键词： outdoors workers,UV Index forecasting methods,Ultra-Violet Index,Ultra-Violet radiation
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Kota Kinabalu, Malaysia (2018.10.7-2018.10.10)] 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Automated Planning of Rooftop PV Systems with Aerial Image Processing
摘要： The increasing prevalence of photovoltaic (PV) panels for microgrid and off-grid energy applications makes affordable PV planning an important issue. Since recording rooftop area and dimensions traditionally required on-site measurements, the process was expensive, slow, and hard to scale. This research develops software that uses image processing for roof detection. Satellite images feed into the software, which estimates the rooftop area receiving solar exposure in that area as well as the number of individual buildings receiving solar exposure. In this way, entire villages can be analyzed automatically, and PV installations planned from afar, rather than requiring a human taking measurements of each building from the ground. This research further develops a GUI to accomplish this rooftop classification for users around the globe, making this capability available even to parties with low resources who would benefit from access to electricity. In this way, the study makes planning PV systems feasible and affordable for many scales of installation, from a single home to a city of numerous assorted buildings.
关键词： demand forecasting,solar panels,object detection,photovoltaic systems,microgrids
更新于2025-09-04 15:30:14