A regional solar forecasting approach using generative adversarial networks with solar irradiance maps

The intermittent and stochastic nature of solar resource hinders the integration of solar energy into modern power system. Solar forecasting has become an important tool for better photovoltaic (PV) power integration, effective market design, and reliable grid operation. Nevertheless, most existing solar forecasting methods are dedicated to improving forecasting accuracy at site-level (e.g. for individual PV power plants) regardless of the impacts caused by the accumulated penetration of distributed PV systems. To tackle with this issue, this article proposes a novel generative approach for regional solar forecasting considering an entire geographical region of a flexible spatial scale. Specifically, we create solar irradiance maps (SIMs) for solar forecasting for the first time by using spatial Kriging interpolation with satellite-derived solar irradiance data. The sequential SIMs provide a comprehensive view of how solar intensity varies over time and are further used as the inputs for a multi-scale generative adversarial network (GAN) to predict the next-step SIMs. The generated SIM frames can be further transformed into PV power output through a irradiance-to-power model. A case study is conducted in a 24 × 24 km area of Brisbane to validate the proposed method by predicting of both solar irradiance and the output of behind-the-meter (BTM) PV systems at unobserved locations. The approach demonstrates comparable accuracy in terms of solar irradiance forecasting and better predictions in PV power generation compared to the conventional forecasting models with a highest average forecasting skill of 10.93±2.35% for all BTM PV systems. Thus, it can be potentially used to assist solar energy assessment and power system control in a highly-penetrated region

Antenna Positioning and Beamforming Design for Fluid-Antenna Enabled Multi-user Downlink Communications

This paper investigates a multiple input single output (MISO) downlink communication system in which users are equipped with fluid antennas (FAs). First, we adopt a field-response based channel model to characterize the downlink channel with respect to FAs' positions. Then, we aim to minimize the total transmit power by jointly optimizing the FAs' positions and beamforming matrix. To solve the resulting non-convex problem, we employ an alternating optimization (AO) algorithm based on penalty method and successive convex approximation (SCA) to obtain a sub-optimal solution. Numerical results demonstrate that the FA-assisted communication system performs better than conventional fixed position antennas system

Simulation Study on Quantitative Measurement of Plutonium in MOX Fuel Pellets Based on Neutron Multiplicity

MOX (mixed oxide) fuel plays a positive role in promoting sustainable development in nuclear energy. The quantitative determination of plutonium in mixed MOX fuel is also essential to nuclear security and safeguards. In this regard，neutron multiplicity measurement methods serve as crucial non-destructive testing techniques and play a vital role in quantitatively detecting plutonium in MOX fuel. In order to study the factors that influence the quantification process of plutonium，enhance measurement accuracy，and streamline the process，this study develops a comprehensive and specific simulation system for the multiplicity-based plutonium quantification method in MOX fuel. Based on the AWCC device as the basic model (simulation model with a detection efficiency of 23.89% and a die-away time of 45.42 μs)，the combination of MCNP and MATLAB software is utilized to recombine the detector capture times of fission neutrons and (α，n) neutrons obtained from MCNP software with the particle emission time series obtained by MATLAB sampling，forming a complete pulse time series. After receiving the multiplicity moments of fission neutrons in MOX fuel (vs1=2.157，vs2=3.808，vs3=5.283，vi1=2.855，vi2=6.953，vi3=13.899)，simulated pulse sequence acquisition，multiplicity analysis，and quantitative calculation are performed on four samples with different shapes and sizes. Each sample is measured for 300 s with a pre-delay time of 3 μs，a gate width of 54 μs，a long delay of 2 ms，and repeated three time’s measurements. Additionally，the simulated quantitative calculation results 240Pu are compared with the set values. The results indicate that the mass of 240Pu and the α-value obtained through simulated pulse analysis meet the requirement of less than 5% relative error compared to the actual input values. This work provides technical support for data interpretation in the quantitative measurement of plutonium in MOX fuel

Deep Learning-Based Multi-Step Solar Forecasting for PV Ramp-Rate Control Using Sky Images

Solar forecasting is one of the most promising approaches to address the intermit PV power generation by providing predictions before upcoming ramp events. In this paper, a novel multi-step forecasting (MSF) scheme is proposed for PV power ramp-rate control (PRRC). This method utilizes an ensemble of deep ConvNets without additional time-series models and exogenous variables, thus more suitable for industrial applications. The MSF strategy can make multiple predictions in comparison with a single forecasting point produced by a conventional method while maintaining the same high temporal resolution. Besides, stacked sky images that integrate temporal-spatial (ST) information of cloud motions are used to further improve the forecasting performance. The results demonstrate a favorable forecasting accuracy in comparison to the existing forecasting models with the highest skill score of 17.7%. In the PRRC application, the MSF-based PRRC can detect more ramp-rates violations with a higher control rate of 98.9% compared with the conventional forecasting based control. Thus, the PV generation can be effectively smoothed with less energy curtailment on both clear and cloudy days using the proposed approach

Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences

Lack of factual correctness is an issue that still plagues state-of-the-art summarization systems despite their impressive progress on generating seemingly fluent summaries. In this paper, we show that factual inconsistency can be caused by irrelevant parts of the input text, which act as confounders. To that end, we leverage information-theoretic measures of causal effects to quantify the amount of confounding and precisely quantify how they affect the summarization performance. Based on insights derived from our theoretical results, we design a simple multi-task model to control such confounding by leveraging human-annotated relevant sentences when available. Crucially, we give a principled characterization of data distributions where such confounding can be large thereby necessitating the use of human annotated relevant sentences to generate factual summaries. Our approach improves faithfulness scores by 20\% over strong baselines on AnswerSumm \citep{fabbri2021answersumm}, a conversation summarization dataset where lack of faithfulness is a significant issue due to the subjective nature of the task. Our best method achieves the highest faithfulness score while also achieving state-of-the-art results on standard metrics like ROUGE and METEOR. We corroborate these improvements through human evaluation

Benchmark analysis for robustness of multi-scale urban road networks under global disruptions

To date immunity to disruptions of multi-scale urban road networks (URNs) has not been effectively quantified. This study uses robustness as a meaningful - if partial - representation of immunity. We propose a novel Relative Area Index (RAI) based on traffic assignment theory to quantitatively measure the robustness of URNs under global capacity degradation due to three different types of disruptions, which takes into account many realistic characteristics. We also compare the RAI with weighted betweenness centrality, a traditional topological metric of robustness. We employ six realistic URNs as case studies for this comparison. Our analysis shows that RAI is a more effective measure of the robustness of URNs when multi-scale URNs suffer from global disruptions. This improved effectiveness is achieved because of RAI's ability to capture the effects of realistic network characteristics such as network topology, flow patterns, link capacity, and travel demand. Also, the results highlight the importance of central management when URNs suffer from disruptions. Our novel method may provide a benchmark tool for comparing robustness of multi-scale URNs, which facilitates the understanding and improvement of network robustness for the planning and management of URNs