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Journal articles on the topic 'Very short-term forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 June 2025

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1

Yurdakul, Ogun, Fatih Eser, Fikret Sivrikaya, and Sahin Albayrak. "Very Short-Term Power System Frequency Forecasting." IEEE Access 8 (2020): 141234–45. http://dx.doi.org/10.1109/access.2020.3013165.

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2

Khumma, Kriangkamon, and Kreangsak Tamee. "Very Short-Term Photovoltaic Power Forecasting Using Stochastic Factors." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 13, no. 2 (2020): 188–95. http://dx.doi.org/10.37936/ecti-cit.2019132.198498.

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This paper proposes a photovoltaic (PV) power forecasting model, using the application of a Gaussian blur algorithm ﬁltering technique to estimate power output and the creation of a stochastic forecasting model. As a result, aﬀected power can be forecasted from stochastic factors with machine learning and an artiﬁcial neural network. This model focuses on very short-term forecasting over a ﬁve minute period. As it uses only endogenous data, no exogenous data is needed. To evaluate the model, results were compared to the persistence model, which has good short-term forecasting accuracy. T

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3

Yang, Dazhi, Zhen Ye, Li Hong Idris Lim, and Zibo Dong. "Very short term irradiance forecasting using the lasso." Solar Energy 114 (April 2015): 314–26. http://dx.doi.org/10.1016/j.solener.2015.01.016.

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4

Liu, K., S. Subbarayan, R. R. Shoults, et al. "Comparison of very short-term load forecasting techniques." IEEE Transactions on Power Systems 11, no. 2 (1996): 877–82. http://dx.doi.org/10.1109/59.496169.

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5

Parol, Mirosław, Paweł Piotrowski, and Mariusz Piotrowski. "Very short-term forecasting of power demand of big dynamics objects." E3S Web of Conferences 84 (2019): 01007. http://dx.doi.org/10.1051/e3sconf/20198401007.

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The issue of very short-term forecasting is gaining more and more importance. It covers both the subject of power demand forecasting and forecasting of power generated in renewable energy sources. In particular, for the reason of necessity of ensuring reliable electricity supplies to consumers, it is very important in small energy micro-systems, which are commonly called microgrids. Statistical analysis of data for a sample big dynamics low voltage object will be presented in this paper. The object, in paper author’s opinion, belongs to a class of objects with difficulties in forecasting, in c

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6

Potter, C. W., and M. Negnevitsky. "Very Short-Term Wind Forecasting for Tasmanian Power Generation." IEEE Transactions on Power Systems 21, no. 2 (2006): 965–72. http://dx.doi.org/10.1109/tpwrs.2006.873421.

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7

da Silva, I. N., and L. C. M. de Andrade. "Efficient Neurofuzzy Model to Very Short-Term Load Forecasting." IEEE Latin America Transactions 14, no. 2 (2016): 721–28. http://dx.doi.org/10.1109/tla.2016.7437215.

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8

Charytoniuk, W., and M. S. Chen. "Very short-term load forecasting using artificial neural networks." IEEE Transactions on Power Systems 15, no. 1 (2000): 263–68. http://dx.doi.org/10.1109/59.852131.

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9

Qu, Wenrui, Jinhong Li, Lu Yang, et al. "Short-Term Intersection Traffic Flow Forecasting." Sustainability 12, no. 19 (2020): 8158. http://dx.doi.org/10.3390/su12198158.

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The intersection is a bottleneck in an urban roadway network. As traffic demand increases, there is a growing congestion problem at urban intersections. Short-term traffic flow forecasting is crucial for advanced trip planning and traffic management. However, there are only a handful of existing models for forecasting intersection traffic flow. In addition, previous short-term traffic flow forecasting models usually were for predicting roadway conditions in a very short period, such as one minute or five minutes, which is often too late given that a driver may well be approaching the bottlenec

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10

Ghadi, et al., M. Jabbari. "Short-Term and Very Short-Term Wind Power Forecasting Using a Hybrid ICA-NN Method." International Journal of Computing and Digital Systems 3, no. 1 (2014): 63–70. http://dx.doi.org/10.12785/ijcds/030108.

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11

Dagdougui, Hanane, Fatemeh Bagheri, Hieu Le, and Louis Dessaint. "Neural network model for short-term and very-short-term load forecasting in district buildings." Energy and Buildings 203 (November 2019): 109408. http://dx.doi.org/10.1016/j.enbuild.2019.109408.

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12

Mandal, Ashis Kumar, Rikta Sen, Saptarsi Goswami, and Basabi Chakraborty. "Comparative Study of Univariate and Multivariate Long Short-Term Memory for Very Short-Term Forecasting of Global Horizontal Irradiance." Symmetry 13, no. 8 (2021): 1544. http://dx.doi.org/10.3390/sym13081544.

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Accurate global horizontal irradiance (GHI) forecasting is crucial for efficient management and forecasting of the output power of photovoltaic power plants. However, developing a reliable GHI forecasting model is challenging because GHI varies over time, and its variation is affected by changes in weather patterns. Recently, the long short-term memory (LSTM) deep learning network has become a powerful tool for modeling complex time series problems. This work aims to develop and compare univariate and several multivariate LSTM models that can predict GHI in Guntur, India on a very short-term b

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13

Jyothi, M. Nandana, and P. V. Ramana Rao. "Very-Short Term Wind Power Forecasting through Wavelet Based ANFIS." International Journal of Power Electronics and Drive Systems (IJPEDS) 9, no. 1 (2018): 397. http://dx.doi.org/10.11591/ijpeds.v9.i1.pp397-405.

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This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compar

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14

Ramesh, Babu N., and P. Arulmozhivarman. "Dynamic Neural Network Based Very Short-Term Wind Speed Forecasting." Wind Engineering 38, no. 2 (2014): 121–28. http://dx.doi.org/10.1260/0309-524x.38.2.121.

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15

Carpinone, A., M. Giorgio, R. Langella, and A. Testa. "Markov chain modeling for very-short-term wind power forecasting." Electric Power Systems Research 122 (May 2015): 152–58. http://dx.doi.org/10.1016/j.epsr.2014.12.025.

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16

LUO, Jian, Tao HONG, and Meng YUE. "Real-time anomaly detection for very short-term load forecasting." Journal of Modern Power Systems and Clean Energy 6, no. 2 (2018): 235–43. http://dx.doi.org/10.1007/s40565-017-0351-7.

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17

Jiang, Yuqi, Tianlu Gao, Yuxin Dai, et al. "Very short-term residential load forecasting based on deep-autoformer." Applied Energy 328 (December 2022): 120120. http://dx.doi.org/10.1016/j.apenergy.2022.120120.

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18

Bessani, Michel, Julio A. D. Massignan, Talysson M. O. Santos, João B. A. London, and Carlos D. Maciel. "Multiple households very short-term load forecasting using bayesian networks." Electric Power Systems Research 189 (December 2020): 106733. http://dx.doi.org/10.1016/j.epsr.2020.106733.

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19

Yang, Zhihe, Jiandun Li, Haitao Wang, and Chang Liu. "An Informer Model for Very Short-Term Power Load Forecasting." Energies 18, no. 5 (2025): 1150. https://doi.org/10.3390/en18051150.

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Facing the decarbonization trend in power systems, there appears to be a growing requirement on agile response and delicate supply from electricity suppliers. To accommodate this request, it is of key significance to precisely extrapolate the upcoming power load, which is well acknowledged as VSTLF, i.e., Very Short-Term Power Load Forecasting. As a time series forecasting problem, the primary challenge of VSTLF is how to identify potential factors and their very long-term affecting mechanisms in load demands. With the help of a public dataset, this paper first locates several intensely relate

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20

Boudevillain, Brice, Hervé Andrieu, and Nadine Chaumerliac. "Evaluation of RadVil, a Radar-Based Very Short-Term Rainfall Forecasting Model." Journal of Hydrometeorology 7, no. 1 (2006): 178–89. http://dx.doi.org/10.1175/jhm481.1.

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Abstract A very short-term rainfall forecast model is tested on actual radar data. This model, called RadVil, takes advantages of voluminal radar data through vertically integrated liquid (VIL) water content measurements. The model is tested on a dataset collected during the intensive observation period of the Mesoscale Alpine Program (MAP). Five rain events have been studied during this experiment. The results confirm the interest of VIL for quantitative precipitation forecasting at very short lead time. The evaluation is carried out in qualitative and quantitative ways according to Nash and

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21

Mikhaylov, A. Yu, V. Khare, S. E. Uhunamure, Ts Chang, and D. I. Stepanova. "Bitcoin Price Short-term Forecast Using Twitter Sentiment Analysis." Financial Journal 15, no. 4 (2023): 123–37. http://dx.doi.org/10.31107/2075-1990-2023-4-123-137.

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The goal of the article is to develop an innovative forecasting approach based on the Random Forest and fuzzy logic models for predicting crypto-asset prices (IFSs, PFSs, q-ROFSs). The baseline forecast horizon is 90 days (additional horizons are 30, 60, 120 and 150 days), which allows to estimate the significance of the chosen features and the impact of time on the forecast accuracy. The paper proposes an optimal data selection approach for the Random Forest and fuzzy logic models to improve the prediction of the daily closing price of Bitcoin, using online social network activity, trading pa

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22

Yang, Liwei, Xiaoqing Gao, Jiajia Hua, Pingping Wu, Zhenchao Li, and Dongyu Jia. "Very Short-Term Surface Solar Irradiance Forecasting Based On FengYun-4 Geostationary Satellite." Sensors 20, no. 9 (2020): 2606. http://dx.doi.org/10.3390/s20092606.

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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

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23

Yosefin, Yosefin. "Short Term Load Forecasting Menggunakan Metode Koefisien." KILAT 9, no. 1 (2020): 28–35. http://dx.doi.org/10.33322/kilat.v9i1.761.

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Electrical energy has a very important role in national economic growth. With the electrical energy requirements, it is necessary to operate an economical, reliable and quality system. The creation of a template for operating load forecasting for the Java-Bali system uses a coefficient method to calculate weekly loads, daily loads, and loads per ½ hour which is more user friendly. After this Load Forecasting template is applied, the result is a more efficient load deepening in terms of file size and time, and is more effective with the results of the calculation of electric energy (1.71%), ele

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24

Valldecabres, Laura, Alfredo Peña, Michael Courtney, Lueder von Bremen, and Martin Kühn. "Very short-term forecast of near-coastal flow using scanning lidars." Wind Energy Science 3, no. 1 (2018): 313–27. http://dx.doi.org/10.5194/wes-3-313-2018.

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Abstract. Wind measurements can reduce the uncertainty in the prediction of wind energy production. Today, commercially available scanning lidars can scan the atmosphere up to several kilometres. Here, we use lidar measurements to forecast near-coastal winds with lead times of 5 min. Using Taylor's frozen turbulence hypothesis together with local topographic corrections, we demonstrate that wind speeds at a downstream position can be forecast by using measurements from a scanning lidar performed upstream in a very short-term horizon. The study covers 10 periods characterised by neutral and sta

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25

Yang, Yi, Jie Wu, Yanhua Chen, and Caihong Li. "A New Strategy for Short-Term Load Forecasting." Abstract and Applied Analysis 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/208964.

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Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF) plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to for

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26

Hsiao, Yu Hsiang. "Very-Short-Term Load Forecast for Individual Household Based on Behavior Pattern Induction." Advanced Materials Research 918 (April 2014): 177–82. http://dx.doi.org/10.4028/www.scientific.net/amr.918.177.

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Electricity demand (load) forecasting has been recognizing as the key issue for achieving economic, reliable, and secure power system operation and planning. In the existing studies, two indentations are found in application level and methodology level, respectively. In the application level, the load forecasting for individual household is few. Most applications focus on large spatial region. In the methodology level, the importance of user daily schedule pattern is ignored in the development of load forecasting methods. In this study, a novel approach to model the load of individual househol

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27

Sopeña, Juan Manuel González, Vikram Pakrashi, and Bidisha Ghosh. "Decomposition-Based Hybrid Models for Very Short-Term Wind Power Forecasting." Engineering Proceedings 5, no. 1 (2021): 39. http://dx.doi.org/10.3390/engproc2021005039.

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Wind power forecasting is a tool used in the energy industry for a wide range of applications, such as energy trading and the operation of the grid. A set of models known as decomposition-based hybrid models have stood out in recent times due to promising results in terms of performance. As many publications on this matter are found in the literature, a comparison of these models is difficult, because they are tested under different conditions in terms of data, prediction horizon, and time resolution. In this paper, we provide a comparison unifying these parameters using the main decomposition

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28

Tawn, R., and J. Browell. "A review of very short-term wind and solar power forecasting." Renewable and Sustainable Energy Reviews 153 (January 2022): 111758. http://dx.doi.org/10.1016/j.rser.2021.111758.

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29

Jamaaluddin, J., D. Hadidjaja, I. Sulistiyowati, EA Suprayitno, I. Anshory, and S. Syahrorini. "Very short term load forecasting peak load time using fuzzy logic." IOP Conference Series: Materials Science and Engineering 403 (October 9, 2018): 012070. http://dx.doi.org/10.1088/1757-899x/403/1/012070.

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30

Casagrande, Gustavo Figueredo, Oswaldo Hideo Ando Junior, Mario Orlando Oliveira, Oscar Eduardo Perrone, and Jose Horacio Reversat. "Very Short-Term Electric Load Forecasting Considering Climate and Temporal Variable." International Journal of Automation and Power Engineering 3, no. 1 (2014): 9. http://dx.doi.org/10.14355/ijape.2014.0301.02.

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31

KATZIR, Liran. "The Effect of System Characteristics on Very-Short-Term Load Forecasting." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 11 (2015): 121–25. http://dx.doi.org/10.15199/48.2015.11.31.

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32

Barbieri, Florian, Sumedha Rajakaruna, and Arindam Ghosh. "Very short-term photovoltaic power forecasting with cloud modeling: A review." Renewable and Sustainable Energy Reviews 75 (August 2017): 242–63. http://dx.doi.org/10.1016/j.rser.2016.10.068.

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33

Alamaniotis, Miltiadis, Andreas Ikonomopoulos, and Lefteri H. Tsoukalas. "Evolutionary Multiobjective Optimization of Kernel-Based Very-Short-Term Load Forecasting." IEEE Transactions on Power Systems 27, no. 3 (2012): 1477–84. http://dx.doi.org/10.1109/tpwrs.2012.2184308.

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34

Jiang, Yu, Zhe Song, and Andrew Kusiak. "Very short-term wind speed forecasting with Bayesian structural break model." Renewable Energy 50 (February 2013): 637–47. http://dx.doi.org/10.1016/j.renene.2012.07.041.

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35

Dietrich, Bastian, Jessica Walther, Matthias Weigold, and Eberhard Abele. "Machine learning based very short term load forecasting of machine tools." Applied Energy 276 (October 2020): 115440. http://dx.doi.org/10.1016/j.apenergy.2020.115440.

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36

Shang, Han Lin. "Functional time series approach for forecasting very short-term electricity demand." Journal of Applied Statistics 40, no. 1 (2012): 152–68. http://dx.doi.org/10.1080/02664763.2012.740619.

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37

Cavalcante, Laura, Ricardo J. Bessa, Marisa Reis, and Jethro Browell. "LASSO vector autoregression structures for very short-term wind power forecasting." Wind Energy 20, no. 4 (2016): 657–75. http://dx.doi.org/10.1002/we.2029.

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38

Browell, J., D. R. Drew, and K. Philippopoulos. "Improved very short-term spatio-temporal wind forecasting using atmospheric regimes." Wind Energy 21, no. 11 (2018): 968–79. http://dx.doi.org/10.1002/we.2207.

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39

Kawauchi, Seiji, Hiroaki Sugihara, and Hiroshi Sasaki. "Development of very-short-term load forecasting based on chaos theory." Electrical Engineering in Japan 148, no. 2 (2004): 55–63. http://dx.doi.org/10.1002/eej.10322.

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40

Dakhil, Adel. "Short Term Load Forecasting Based Artificial Neural Network." Iraqi Journal for Electrical and Electronic Engineering 10, no. 1 (2014): 42–47. http://dx.doi.org/10.37917/ijeee.10.1.5.

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Present study develops short term electric load forecasting using neural network; based on historical series of power demand the neural network chosen for this network is feed forward network, this neural network has five input variables ( hour of the day, the day of the week, the load for the previous hour, the load of the pervious day, the load for the previous week). Short term load forecast is very important due to accurate for power system operation and analysis system security among other mandatory function. The trained artificial neural network shows good accuracy and robust in forecast

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41

Lee, WonJun, Munsu Lee, Byung-O. Kang, and Jaesung Jung. "24 hour Load Forecasting using Combined Very-short-term and Short-term Multi-Variable Time-Series Model." Transactions of The Korean Institute of Electrical Engineers 66, no. 3 (2017): 493–99. http://dx.doi.org/10.5370/kiee.2017.66.3.493.

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42

Ajay, Bhardwaj*1 Aarushi Gupta2 Aditya Kumar Mehta3 Arun Kumar Sahu4 Garvit Sharma2. "SHORT TERM LOAD FORECASTING USING ARTIFICIAL NEURAL NETWORK." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 4 (2019): 448–53. https://doi.org/10.5281/zenodo.2655106.

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Accurate short term load forecasting is an essential task in power system planning, operation, and control. This paper discusses significant role of artificial intelligence (AI) in short -term load forecasting (STLF). A new artificial neural network (ANN) has been designed to compute the forecasted load. The ANN model is trained on hourly data from the ISO New England market from 2004 to 2008 and tested on out-of-sample data from 2009. Load forecast for ISO New England market is much better with temperature data as input than without taking it. This is due to the fact that temperature and weat

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43

Collino, Elena, and Dario Ronzio. "Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System." Energies 14, no. 3 (2021): 789. http://dx.doi.org/10.3390/en14030789.

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The relentless spread of photovoltaic production drives searches of smart approaches to mitigate unbalances in power demand and supply, instability on the grid and ensuring stable revenues to the producer. Because of the development of energy markets with multiple time sessions, there is a growing need of power forecasting for multiple time steps, from fifteen minutes up to days ahead. To address this issue, in this study both a short-term-horizon of three days and a very-short-term-horizon of three hours photovoltaic production forecasting methods are presented. The short-term is based on a m

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44

Filik, Tansu. "Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting." Energies 9, no. 3 (2016): 168. http://dx.doi.org/10.3390/en9030168.

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45

Kawauchi, Seiji, Hiroaki Sugihara, and Hiroshi Sasaki. "A Development of Very Short-Term Load Forecasting Based on Chaos Theory." IEEJ Transactions on Power and Energy 123, no. 5 (2003): 646–53. http://dx.doi.org/10.1541/ieejpes.123.646.

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46

Yamamoto, Toshiyuki, Akihiko Yokoyama, Yuusuke Honda, Hiroshi Yabuta, and Kiyoshi Yoshida. "A Very Short-Term Load Forecasting Method for Economic Load Dispatching Control." IEEJ Transactions on Power and Energy 125, no. 1 (2005): 39–44. http://dx.doi.org/10.1541/ieejpes.125.39.

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47

Aryaputera, Aloysius W., Dazhi Yang, Lu Zhao, and Wilfred M. Walsh. "Very short-term irradiance forecasting at unobserved locations using spatio-temporal kriging." Solar Energy 122 (December 2015): 1266–78. http://dx.doi.org/10.1016/j.solener.2015.10.023.

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48

Guan, Che, Peter B. Luh, Laurent D. Michel, Yuting Wang, and Peter B. Friedland. "Very Short-Term Load Forecasting: Wavelet Neural Networks With Data Pre-Filtering." IEEE Transactions on Power Systems 28, no. 1 (2013): 30–41. http://dx.doi.org/10.1109/tpwrs.2012.2197639.

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49

Walther, Jessica, Dario Spanier, Niklas Panten, and Eberhard Abele. "Very short-term load forecasting on factory level – A machine learning approach." Procedia CIRP 80 (2019): 705–10. http://dx.doi.org/10.1016/j.procir.2019.01.060.

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50

Yang, Hong Ying, Hao Ye, Guizeng Wang, Junaid Khan, and Tongfu Hu. "Fuzzy neural very-short-term load forecasting based on chaotic dynamics reconstruction." Chaos, Solitons & Fractals 29, no. 2 (2006): 462–69. http://dx.doi.org/10.1016/j.chaos.2005.08.095.

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