Relevant bibliographies by topics / Power predictions

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Power predictions'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Power predictions"

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Beardsell, Alec. "Power predictions." Physics World 33, no. 5 (2020): 26. http://dx.doi.org/10.1088/2058-7058/33/5/25.

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Zhuang, Wei, Zhiheng Li, Ying Wang, Qingyu Xi, and Min Xia. "GCN–Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting." Applied Sciences 14, no. 5 (2024): 2181. http://dx.doi.org/10.3390/app14052181.

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Predicting photovoltaic (PV) power generation is a crucial task in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation prediction, traditional deep learning methods often generate predictions for long sequences one by one, significantly impacting the efficiency of model predictions. As the scale of photovoltaic power stations expands and the demand for predictions increases, this sequential prediction approach may lead to slow prediction speeds, making it difficult to me
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Wu, Xinhua, Nan Chen, Qianyun Du, Shuangshuang Mao, and Xiaoming Ju. "Short-term wind power prediction model based on ARMA-GRU-QPSO and error correction." Journal of Physics: Conference Series 2427, no. 1 (2023): 012028. http://dx.doi.org/10.1088/1742-6596/2427/1/012028.

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Abstract Power system dispatch benefits from accurate wind power predictions. To increase the prediction precision for wind power, this paper proposes a combined model for predicting short-term wind power based on the autoregressive moving average-gated recurrent unit (ARMA-GRU). Firstly, we build the ARMA model and GRU model respectively to predict wind power. Then we optimize the combined model’s weights by quantum particle swarm algorithm (QPSO). Finally, we build an error correction model for the prediction errors to acquire the final results for the wind power predictions. Our experimenta
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Shen, Runjie, Ruimin Xing, Yiying Wang, Danqiong Hua, and Ming Ma. "Ultra-short-term prediction method of photovoltaic electric field power based on ground-based cloud image segmentation." E3S Web of Conferences 185 (2020): 01052. http://dx.doi.org/10.1051/e3sconf/202018501052.

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As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by s
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Jin, Xue-Bo, Hong-Xing Wang, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, and Jian-Lei Kong. "Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization." Complexity 2020 (September 14, 2020): 1–14. http://dx.doi.org/10.1155/2020/4346803.

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The power load prediction is significant in a sustainable power system, which is the key to the energy system’s economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load data are decomposed into components; then, the gated recurrent unit (GRU) network, with t
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Maitanova, Nailya, Jan-Simon Telle, Benedikt Hanke, et al. "A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports." Energies 13, no. 3 (2020): 735. http://dx.doi.org/10.3390/en13030735.

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A fully automated transferable predictive approach was developed to predict photovoltaic (PV) power output for a forecasting horizon of 24 h. The prediction of PV power output was made with the help of a long short-term memory machine learning algorithm. The main challenge of the approach was using (1) publicly available weather reports without solar irradiance values and (2) measured PV power without any technical information about the PV system. Using this input data, the developed model can predict the power output of the investigated PV systems with adequate accuracy. The lowest seasonal m
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Liu, Shipeng, Dejun Ning, and Jue Ma. "TCNformer Model for Photovoltaic Power Prediction." Applied Sciences 13, no. 4 (2023): 2593. http://dx.doi.org/10.3390/app13042593.

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Despite the growing capabilities of the short-term prediction of photovoltaic power, we still face two challenges to longer time-range predictions: error accumulation and long-term time series feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic power, this paper proposes a seq2seq prediction model TCNformer, which outperforms other state-of-the-art (SOTA) algorithms by introducing variable selection (VS), long- and short-term time series feature extraction (LSTFE), and one-step temporal convolutional network (TCN) decoding. A VS module employs corr
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Guo, Wei, Li Xu, Tian Wang, Danyang Zhao, and Xujing Tang. "Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data." Sensors 24, no. 5 (2024): 1593. http://dx.doi.org/10.3390/s24051593.

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Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel
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Cahyadi, Catra Indra, Suwarno Suwarno, Aminah Asmara Dewi, Musri Kona, Muhammad Arif, and Muhammad Caesar Akbar. "Solar Prediction Strategy for Managing Virtual Power Stations." International Journal of Energy Economics and Policy 13, no. 4 (2023): 503–12. http://dx.doi.org/10.32479/ijeep.14124.

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The potential for solar power is available in Indonesia because it is located on the equator, with good sunshine all year round. The Indonesian government is currently actively developing a solar power plant while still looking at the consequences of development, especially on the surrounding environment. It is necessary to pay attention so that it does not disturb the surrounding environment, which can also cause climate change. The city of Medan is one of the largest cities in Indonesia, which has direct exposure to sunlight which is quite promising for predicting solar power plants in the f
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Xhaferaj, Blenard. "INVESTIGATION ON SOME CONVENTIONAL HULLS FORMS OF THE PREDICTIVE ACCURACY OF A PARAMETRIC SOFTWARE FOR PRELIMINARY PREDICTIONS OF RESISTANCE AND POWER." Brodogradnja 73, no. 1 (2022): 1–22. http://dx.doi.org/10.21278/brod73101.

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Preliminary prediction of resistance and power is a fundamental aspect of the ship design process since they directly influence the developments of the design process, fuel consumption and costs, and environmental impact from the early design stage. Parametric predictions of resistance and power, based mainly on statistical regression models that are also ideal for computer programming, are often performed during initial design stages, providing rapid predictions and optimisations for minimum resistance. The paper aims to present the results of the comparative analysis on some conventional hul
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Dissertations / Theses on the topic "Power predictions"

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Lange, Matthias. "Analysis of the uncertainty of wind power predictions." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=969985789.

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SATO, Ken-ichi, Hiroshi HASEGAWA, and Hiroyuki ITO. "Router Power Reduction through Dynamic Performance Control Based on Traffic Predictions." 電子情報通信学会, 2012. https://search.ieice.org/.

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Werngren, Simon. "Comparison of different machine learning models for wind turbine power predictions." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362332.

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The goal of this project is to compare different machine learning algorithms ability to predict wind power output 48 hours in advance from earlier power data and meteorological wind speed predictions. Three different models were tested, two autoregressive integrated moving average (ARIMA) models one with exogenous regressors one without and one simple LSTM neural net model. It was found that the ARIMA model with exogenous regressors was the most accurate while also beingrelatively easy to interpret and at 1h 45min 32s had a comparatively short training time. The LSTM was less accurate, harder
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Kossmann, de Menezes Anna Carolina. "Improving predictions of operational energy performance through better estimates of small power consumption." Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/13549.

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This Engineering Doctorate aims to understand the factors that generate variability in small power consumption in commercial office buildings in order to generate more representative, building specific estimates of energy consumption. Current energy modelling practices in England are heavily focussed on simplified calculations for compliance with Building Regulations, which exclude numerous sources of energy use such as small power. When considered, estimates of small power consumption are often based on historic benchmarks, which fail to capture the significant variability of this end-use, as
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COCINA, VALERIA CONCETTA. "Economy of grid-connected photovoltaic systems and comparison of irradiance/electric power predictions vs. experimental results." Doctoral thesis, Politecnico di Torino, 2014. http://hdl.handle.net/11583/2538892.

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This thesis is focused on various aspects concerning the Distributed Generation (DG) from Renewable Energy Sources (RES) and in particular from PhotoVoltaics (PV). The PV generation strongly depends on weather conditions (irradiance and temperature), therefore the solar irradiance forecast is very important for grid-connected PV systems. The PV power injected into the grid is concentrated during sunlight hours, in which the maximum peak load demand occurs and, as a consequence, an impact on the electrical system occurs. The task of the Transmission System Operator (TSO) is to ensure a constant
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Herrin, Judith Mitchell. "Clients' Evaluations of Lawyers: Predictions from Procedural Justice Ratings and Interactional Styles of Lawyers." Diss., This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-01292008-112254/.

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Alexander, Richard. "Analysis of Aircraft Power Systems, Including System Modeling and Energy Optimization, with Predictions of Future Aircraft Development." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523541008209354.

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Soldi, James D. "Arc rate predictions and flight data analysis for the photovoltaic array space power plus diagnostics (PASP Plus) experiment." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11147.

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Zastrau, David [Verfasser]. "Estimation of Uncertainty of Wind Energy Predictions : With Application to Weather Routing and Wind Power Generation / David Zastrau." Frankfurt a.M. : Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2017. http://d-nb.info/1127484524/34.

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Lledó, Ponsatí Llorenç. "Climate variability predictions for the wind energy industry: a climate services perspective." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/670882.

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In order to mitigate the climate change effects, the world is undergoing an energy transition from polluting sources towards renewable energies. This transition is turning the electricity system more dependent on atmospheric conditions and more prone to suffer the effects of climate variability. The atmospheric circulation is changing in certain aspects due to increasing concentrations of greenhouse gases in the atmosphere, but it also varies from year to year due to natural variability processes occurring in the Earth system at timescales of weeks, months and years. The atmosphere intera
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Books on the topic "Power predictions"

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United States. National Aeronautics and Space Administration., ed. Power-on performance predictions for a complete generic hypersonic vehicle configuration. MCAT Institute, 1991.

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United States. National Aeronautics and Space Administration., ed. Power-on performance predictions for a complete generic hypersonic vehicle configuration. MCAT Institute, 1991.

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U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research and OECD Halden Reactor Project, eds. International HRA empirical study--phase 2 report: Results from comparing HRA method predictions to simulator data from SGTR scenarios. U.S. Nuclear Regulatory Commission, 2009.

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Bodaly, R. A. The mercury problem in hydro-electric reservoirs with predictions of mercury burdens in fish in the proposed Grande Baleine Complex, Québec. North Wind Information Services, 1992.

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McKay, Michael D. Evaluating prediction uncertainty. The Commission, 1995.

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Mason, Lee S. A Solar Dynamic power option for Space Solar Power. National Aeronautics and Space Administration, Glenn Research Center, 1999.

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National Renewable Energy Laboratory (U.S.) and IEEE Energy Conversion Congress and Exposition (2012 : Raleigh, N.C.), eds. Wind power plant prediction by using neural networks: Preprint. National Renewable Energy Laboratory, 2012.

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Radojčić, Dejan, Milan Kalajdžić, and Aleksandar Simić. Power Prediction Modeling of Conventional High-Speed Craft. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30607-6.

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York, Richard. Branch prediction strategies for low power microprocessor design. Universityof Manchester, 1994.

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G, Sigari, Costi T, Michigan State University. Division of Engineering Research., and United States. National Aeronautics and Space Administration., eds. Effect of accuracy of wind power prediction on power system operator: Final report. College of Engineering, Michigan State University, 1985.

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Book chapters on the topic "Power predictions"

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Wang, Zhijun, Riyu Cong, Ruihong Wang, and Zhihui Wang. "Digital Twin for Power Load Forecasting." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2409-6_36.

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Abstract In this work, a novel Digital Twin model using attention mechanism integrated with LSTM to forecast the future power load of a specific user is developed. The power load prediction research is done in detail by taking into account important factors such as temperature, humidity, and the price of electricity. Therefore, LSTM networks are adopted for deep learning of the historical power load data, while the attention mechanism is used to assign weights to the significance of various factors that affect the power load and make better predictions of the future power load. The results of
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Xiao, Yong, Xingming Zhou, and Kun Deng. "Making Power-Efficient Data Value Predictions." In Advances in Computer Systems Architecture. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11572961_25.

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Gupta, Nandkishor, K. G. Sharma, A. Mangal, K. C. Sharma, and R. A. Gupta. "Solar Power Predictions in Stochastics Framework." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4103-9_19.

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Radojčić, Dejan, Milan Kalajdžić, and Aleksandar Simić. "Resistance and Dynamic Trim Predictions." In Power Prediction Modeling of Conventional High-Speed Craft. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30607-6_8.

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Ambach, Daniel, and Carsten Croonenbroeck. "Obtaining Superior Wind Power Predictions from a Periodic and Heteroscedastic Wind Power Prediction Tool." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13881-7_25.

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Treiber, Nils André, and Oliver Kramer. "Evolutionary Turbine Selection for Wind Power Predictions." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11206-0_26.

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Bruce, Robert D., and Charles T. Moritz. "Sound Power Level Predictions for Industrial Machinery." In Encyclopedia of Acoustics. John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470172520.ch86.

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Radojčić, Dejan. "Resistance and Dynamic Trim Predictions." In Reflections on Power Prediction Modeling of Conventional High-Speed Craft. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94899-7_3.

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Wolff, Björn, Elke Lorenz, and Oliver Kramer. "Statistical Learning for Short-Term Photovoltaic Power Predictions." In Computational Sustainability. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31858-5_3.

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Ravva, Srinivasa Rao, Kannan N. Iyer, Aniket Gupta, Gurav Kumar, Avinash J. Gaikwad, and S. K. Gupta. "Comparison of Lumped Parameter and CFD Code Predictions: Sump Evaporation Phenomena." In Fluid Mechanics and Fluid Power – Contemporary Research. Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2743-4_160.

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Conference papers on the topic "Power predictions"

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Li, Bo, and Shuya Xing. "Advancing Photovoltaic Power Generation Predictions Using Artificial Neural Networks." In 2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2024. https://doi.org/10.1109/iccasit62299.2024.10828062.

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Santana, Vinicius V., Carine M. Rebello, Erbet A. Costa, et al. "Recurrent Deep Learning Models for Multi-step Ahead Prediction: Comparison and Evaluation for Real Electrical Submersible Pump (ESP) System." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.107762.

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Predicting processes� future behavior based on past data is vital for automatic control and dynamic optimization in engineering. Recent advances in deep learning, particularly Artificial Neural Networks, have improved predictions in various engineering fields. Recurrent Neural Networks (RNNs) are well-suited for time series data, as they naturally evolve through dynamic systems with recurrent updates. Despite their high predictive power, RNNs may underperform if their training ignores the model's future application. In Model Predictive Control, for example, the model evolves over time using on
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Hahn, Luzia, and Peter Eberhard. "Transient simulation of high-power dynamical-thermoelastic-optical systems." In Optical Modeling and Performance Predictions XII, edited by Mark A. Kahan. SPIE, 2022. http://dx.doi.org/10.1117/12.2632227.

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Plaza, David, Rubén Paredes, Jonathan Morán, and Raju Datla. "Performance Assessment of Warped Bottom Planing Hulls Using Machine Learning Techniques." In SNAME Power Boat Symposium. SNAME, 2024. http://dx.doi.org/10.5957/cpbs-2024-005.

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Determining resistance and trim of planing hulls in the early design phase has traditionally relied on semi-empirical prediction models. However, in the case of hulls with warped bottoms, resistance predictions frequently require validation with tank testing or numerical simulations. The present work focuses on predicting resistance and trim of planing hulls using five machine learning methods trained using data from available experimental model tests of warped bottom hulls. This database contains large systematic data from Series 50, US Coast Guard, and Naples Systematic Series which cover a
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Shamee, Bishara, Amirhossein Mohajerin-Ariaei, Ahmed Almaiman, Yinwen Cao, Fatemeh Alishahi, and Alan Willner. "Weighted raised cosine waveform with reduced peak to average power ratio for optical transmission." In Optical Modeling and Performance Predictions X, edited by Marie B. Levine-West and Mark A. Kahan. SPIE, 2018. http://dx.doi.org/10.1117/12.2326518.

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Zhou, Shaopu, Sicheng Lu, Takeo Maruyama, and Zhiwei Zhou. "Design of face-to-face optical wireless power transmission system based on robot arm visual tracking." In Optical Modeling and Performance Predictions XIII, edited by Mark A. Kahan. SPIE, 2023. http://dx.doi.org/10.1117/12.2677391.

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Zhang, Zhibo, Hongtao Zheng, Zhiming Li, Yajun Li, Gang Pan, and Xi Chen. "A New Method for Numerical Prediction of Lean Blowout in Aero-Engine Combustor." In ASME 2013 Power Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/power2013-98199.

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Lean blowout (LBO) is one of the most important parameters on combustor performance. A new method named Feature-Section-criterion (FSC) for predicting LBO of aero-engine annular combustor has been put forward in the present work. A CFD software FLUENT has been used to simulate the combustion flow field of an annular combustor. The prediction of LBO with FSC has been done in this paper and the effects of flow velocity, air temperature and droplet averaged-diameter on the LBO of aero-engine combustor have been discussed by using of FSC. The results show that the predictions of FSC are in agreeme
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Assaf, Roy, and Anika Schumann. "Explainable Deep Neural Networks for Multivariate Time Series Predictions." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/932.

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We demonstrate that CNN deep neural networks can not only be used for making predictions based on multivariate time series data, but also for explaining these predictions. This is important for a number of applications where predictions are the basis for decisions and actions. Hence, confidence in the prediction result is crucial. We design a two stage convolutional neural network architecture which uses particular kernel sizes. This allows us to utilise gradient based techniques for generating saliency maps for both the time dimension and the features. These are then used for explaining which
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Keys, Catherine, Brian Watkins, Cierra Coughlin, Bao Hoang, and Samuel Beyene. "Maxar EOR Radiation to Power Predictions." In AIAA Scitech 2020 Forum. American Institute of Aeronautics and Astronautics, 2020. http://dx.doi.org/10.2514/6.2020-2014.

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Yusop, Nadiahnor Md, Gordon E. Andrews, Derek B. Ingham, I. M. Khalifa, Mike C. Mkpadi, and Mohammed Pourkashanian. "Predictions of Adiabatic Film Cooling Effectiveness for Effusion Film Cooling." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27467.

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This paper presents computational predictions of adiabatic film cooling effectiveness for effusion cooling systems with 90° and 30° holes. Predictions are performed for a range of coolant injection mass flow rates per unit surface area, G, of 0.1kg/sm2 - 1.6 kg/sm2 for 90° holes with constant pitch-to-diameter ratio of X/D = 11 and 10 rows of holes and for 30° inclined holes with X/D = 11 and 15 rows of holes over a 152mm surface length. The computational works performed are steady-state and the turbulent governing equations are solved by a control-volume-based finite difference method with se
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Reports on the topic "Power predictions"

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Alviarez, Vanessa, Michele Fioretti, Ken Kikkawa, and Monica Morlacco. Two-Sided Market Power in Firm-to-Firm Trade. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003493.

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Firms in global value chains (GVCs) are granular and exert bargaining power over the terms of trade. We show that these features are crucial to understanding the well-established variation in prices and pass-through across importers and exporters. We develop a novel theory of prices in GVCs, which tractably nests a wide range of bilateral concentration and bargaining power configurations. We test and evaluate the models predictions using a novel dataset merging transaction-level U.S. import data with balance sheet data for both U.S. importers and foreign exporters. Our pricing framework enhanc
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Bond, R. A. ,. Jr. Predictions and acceptance criteria for K Reactor startup and power ascension. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/5083811.

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Bond, R. A. Jr. Predictions and acceptance criteria for K Reactor startup and power ascension. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/5084698.

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Bond, R. A. ,. Jr. Predictions and acceptance criteria for K Reactor startup and power ascension. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/10164098.

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Arshavsky, Igor, Hisham Sarsour, Paul Turinsky, et al. Accuracy Enhancement of Nuclear Power Plant Simulators Utilizing High Accuracy Simulation Predictions. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2348910.

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Alviarez, Vanessa, Michele Fioretti, Ken Kikkawa, and Monica Morlacco. Two-Sided Market Power in Firm-to-Firm Trade. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0004746.

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We develop a quantitative theory of prices in firm-to-firm trade with bilateral negotiations and two-sided market power. Markups reflect oligopoly and oligopsony forces, with relative bargaining power as weight. Cost pass-through elasticities into import prices can be incomplete or complete, depending on the exporters and importers bargaining power and market shares. In U.S. import data, we find that U.S. importers have substantial market power and disproportionate leverage in price negotiations. The estimated model produces accurate predictions of the impact of Trump tariffs on pair-level pri
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Bond, R. A. Jr. Predictions and acceptance criteria for K Reactor startup and power ascension. Addendum 1. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/10163780.

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Frost, R. L., C. Boman, and K. A. Niemer. GRIMHX predictions of axial power shapes and xenon worth with 3-D depletion modeling. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/6960205.

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Frost, R. L., C. Boman, and K. A. Niemer. GRIMHX predictions of axial power shapes and xenon worth with 3-D depletion modeling. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10114326.

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Seema, Seema, Andreas Theocharis, and Andreas Kassler. Evaluate Temporal and Spatio-Temporal Correlations for Different Prosumers Using Solar Power Generation Time Series Dataset. Karlstad University, 2024. http://dx.doi.org/10.59217/yjll7238.

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Abstract:
This study investigates the temporal and spatio-temporal correlations of solar power generation among different prosumers of Uppsala and Halmstad, Sweden. Using solar power generation data from seven prosumer in Uppsala and five in Halmstad, we evaluate the correlation of solar power production generation at specific locations correlates with itself over different time lags (autocorrelation). In addition, we examine the spatiotemporal correlations of solar power production at various locations over a range of lags using time shifted cross correlation. These spatio-temporal correlations can fac
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