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Journal articles on the topic 'Neural calendar'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Allawi, Ziyad T. "A Pattern-Recognizer Artificial Neural Network for the Prediction of New Crescent Visibility in Iraq." Computation 10, no. 10 (2022): 186. http://dx.doi.org/10.3390/computation10100186.

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Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of

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Meng, Ming, Dong Xiao Niu, Wei Sun, and Wei Shang. "Research on Monthly Electric Energy Demand Forecasting under the Influence of Two Calendars." Applied Mechanics and Materials 20-23 (January 2010): 963–68. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.963.

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Monthly electric energy demand forecasting plays an important role for the running of power system. China has two tow calendars and they works at the same time. Holidays designed by the lunar calendar affect the regularity of monthly electric load recorded only by the Gregorian one. The normal fuzzy transform is advanced here to quantitatively describe the impact of the Spring Festival and further divided the influence into Jan. and Feb. After excluding the influence, the amended historical data are adopted to training RBF neural network. Experiment results show that because the regularity of

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Vasu, Sharma, and Satish Saini Dr. "Prediction of Power Loss in Grid Using Neural Network." International Journal of Innovative Research in Engineering and Management (IJIREM) 10, no. 04 (2023): 77–85. https://doi.org/10.55524/ijirem.2023.10.4.9.

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This Paper proposes a data-driven approach for grid loss prediction in power systems. It utilizes a comprehensive dataset with relevant features such as grid load, temperature forecasts, and calendar data. The dataset is pre-processed by handling missing values, normalizing features, and encoding cyclic calendar features. A Long Short-Term Memory (LSTM) recurrent neural network is employed for the prediction model, capturing temporal dependencies and generating forecasts of grid loss two hours ahead. The model is trained using mean absolute error (MAE) as the loss function and optimized throug

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Hofman, Michel A. "The brains calendar: neural mechanisms of seasonal timing." Biological Reviews 79, no. 1 (2004): 61–77. http://dx.doi.org/10.1017/s1464793103006250.

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Nhita, Fhira, Adiwijaya, U. N. Wisesty, and Izzatul Ummah. "PLANTING CALENDAR FORECASTING SYSTEM USING EVOLVING NEURAL NETWORK." Far East Journal of Electronics and Communications 14, no. 2 (2015): 81–92. http://dx.doi.org/10.17654/fjecjun2015_081_092.

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Chenchen, Li, Fu Yuan, Wang Xiaolin, Dong Shulong, and Song Ziwen. "MATLAB network model simulation based on BP neural network." Journal of Scientific and Engineering Research 8, no. 7 (2021): 96–104. https://doi.org/10.5281/zenodo.10608864.

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<strong>Abstract</strong> With the development of china's economy this year, the demand for automobiles is increasing, so the number of car accidents in the country each year is also more and more concerned, this paper first collects the number of car accidents in the national calendar year, and then uses these data to make predictions with MATLAB using BP neural network model and improved radial base function network, and finally analyzes the advantages and disadvantages of the model according to the prediction results, so as to achieve a deep understanding of the principle and application of

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Fitriana, Gita Fadila, and Novian Adi Prasetyo. "Rice Planting Calendar Application Development using Scrum." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 16, no. 2 (2022): 169. http://dx.doi.org/10.22146/ijccs.70155.

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Indonesia is an agricultural country that produces more rice commodities than secondary crops. Many people who work as farmers choose the land to plant rice. Farmers experience several obstacles in determining the correct planting time to improve the rice harvest quality. A planting calendar is a method used by farmers to determine the scheduling of planting for one year. The rice planting calendar works based on rainfall and climate patterns. With the help of the latest technology, determining the rice planting calendar can be done quickly. The utilization of computer technology and algorithm

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Jiang, Hui, Mingzhe Zhu, Jun Li, et al. "Data Mining Based on Chinese Traditional Calendar in the Han Dynasty Yang Mausoleum Museum." Applied Sciences 9, no. 24 (2019): 5442. http://dx.doi.org/10.3390/app9245442.

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The Outer Burial Pits of the Han Dynasty Yang Mausoleum is the first fully enclosed site museum in China. The Internet of things sensor installed in the pavilion has accumulated more than 7,000,000 heterogeneous data. Traditional algorithms, such as temperature prediction model, only use statistical value to predict the trend of temperature change, so the data utilization rate is insufficient. In addition, the accuracy of prediction model is also relatively low. The extreme learning machine is a single layer feedback neural network learning method. Using extreme learning machine to analyze and

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Nguyen, Tuan-Dung, and Thanh-Phuong Nguyen. "LOAD FORECASTING FOR MONTHS OF THE LUNAR NEW YEAR HOLIDAY USING STANDARDIZED LOAD PROFILE AND SUPPORT REGRESSION VECTOR: CASE STUDY HO CHI MINH CITY." JOURNAL OF TECHNOLOGY & INNOVATION 1, no. 1 (2020): 01–05. http://dx.doi.org/10.26480/jtin.01.2021.01.05.

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Load forecasting plays an important role in building business strategies, ensuring reliability and safe operation for any electrical system. There are many different methods, including: regression models, time series, neural networks, expert systems, fuzzy logic, machine learning and statistical algorithms used for short-term forecasts. However, the practical requirement is how to minimize the forecast errors to prevent power shortages or wastage in the electricity market and limit risks. For Asian countries (such as Vietnam) that use lunar calendar, one of the most difficult and unpredictable

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Dung Nguyen, Tuan, and Thanh Phuong Nguyen. "LOAD FORECASTING FOR MONTHS OF THE LUNAR NEW YEAR HOLIDAY USING STANDARDIZED LOAD PROFILE AND SUPPORT REGRESSION VECTOR: CASE STUDY HO CHI MINH CITY." JOURNAL OF TECHNOLOGY & INNOVATION 1, no. 1 (2020): 01–06. http://dx.doi.org/10.26480/jtin.01.2021.01.06.

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Load forecasting plays an important role in building business strategies, ensuring reliability and safe operation for any electrical system. There are many different methods, including: regression models, time series, neural networks, expert systems, fuzzy logic, machine learning and statistical algorithms used for short-term forecasts. However, the practical requirement is how to minimize the forecast errors to prevent power shortages or wastage in the electricity market and limit risks. For Asian countries (such as Vietnam) that use lunar calendar, one of the most difficult and unpredictable

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Trull, Óscar, J. García-Díaz, and Alicia Troncoso. "Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter." Energies 12, no. 6 (2019): 1083. http://dx.doi.org/10.3390/en12061083.

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Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of activity. However, the accuracy of the forecasts is enormously subject to the calendar effect. The multiple seasonal Holt–Winters models are widely used due to the great precision and simplicity that they offer. Usually, these models relate this calendar effect to external variables that contribute to modification of their forecasts a posteriori. In this work, a new point of view

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Suhartono, Made Gde Meranggi Dana I, and Puteri Rahayu Santi. "Hybrid model for forecasting space-time data with calendar variation effects." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 1 (2019): 118–30. https://doi.org/10.12928/TELKOMNIKA.v17i1.10096.

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The aim of this research is to propose a new hybrid model, i.e. Generalized Space-Time Autoregressive with Exogenous Variable and Neural Network (GSTARX-NN) model for forecasting space-time data with calendar variation effect. GSTARX model represented as a linear component with exogenous variable particularly an effect of calendar variation, such as Eid Fitr. Whereas, NN was a model for handling a nonlinear component. There were two studies conducted in this research, i.e. simulation studies and applications on monthly inflow and outflow currency data in Bank Indonesia at East Java region. The

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Fehr, Thorsten, Gregory L. Wallace, Peter Erhard, and Manfred Herrmann. "The neural architecture of expert calendar calculation: A matter of strategy?" Neurocase 17, no. 4 (2011): 360–71. http://dx.doi.org/10.1080/13554794.2010.532135.

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Lopes, Gustavo. "The wisdom of crowds in forecasting at high-frequency for multiple time horizons: A case study of the Brazilian retail sales." Brazilian Review of Finance 20, no. 2 (2022): 77–115. http://dx.doi.org/10.12660/rbfin.v20n2.2022.85016.

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This case study compares the forecasting accuracy obtained for four daily Brazilian retail sales indexes at four time prediction horizons. The performance of traditional time series forecasting models, artificial neural network architectures and machine learning algorithms were compared in other to evaluate the existence of a single best performing model. Afterwards, ensemble methods were added to model comparison to verify if accuracy improvement could be obtained. Evidence found in this case study suggests that a consistent forecasting strategy exists for the Brazilian retail indexes by appl

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Lohachov, Mykhailo, Ryoji Korei, Kazuo Oki, et al. "RNN-Based Approach for Broccoli Harvest Time Forecast." Agronomy 14, no. 2 (2024): 361. http://dx.doi.org/10.3390/agronomy14020361.

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This article investigates approaches for broccoli harvest time prediction through the application of various machine learning models. This study’s experiment is conducted on a commercial farm in Ecuador, and it integrates in situ weather and broccoli growing cycle observations made over seven years. This research incorporates models such as the persistence, thermal, and calendar models, demonstrating their strengths and limitations in calculating the optimal broccoli harvest day. Additionally, Recurrent Neural Network (RNN) models with Long Short-term Memory (LSTM) layers were developed, showc

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Azkue, Markel, Mattin Lucu, Egoitz Martinez-Laserna, and Iosu Aizpuru. "Calendar Ageing Model for Li-Ion Batteries Using Transfer Learning Methods." World Electric Vehicle Journal 12, no. 3 (2021): 145. http://dx.doi.org/10.3390/wevj12030145.

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Getting accurate lifetime predictions for a particular cell chemistry remains a challenging process, largely dependent on time and cost-intensive experimental battery testing. This paper proposes a transfer learning (TL) method to develop LIB ageing models, which allow for the leveraging of experimental laboratory testing data previously obtained for a different cell technology. The TL method is implemented through Neural Networks models, using LiNiMnCoO2/C laboratory ageing data as a baseline model. The obtained TL model achieves an 1.01% overall error for a broad range of operating condition

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Putri, J. A., Suhartono Suhartono, H. Prabowo, N. A. Salehah, D. D. Prastyo, and Setiawan Setiawan. "Forecasting Currency in East Java: Classical Time Series vs. Machine Learning." Indonesian Journal of Statistics and Its Applications 5, no. 2 (2021): 284–303. http://dx.doi.org/10.29244/ijsa.v5i2p284-303.

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Most research about the inflow and outflow currency in Indonesia showed that these data contained both linear and nonlinear patterns with calendar variation effect. The goal of this research is to propose a hybrid model by combining ARIMAX and Deep Neural Network (DNN), known as hybrid ARIMAX-DNN, for improving the forecast accuracy in the currency prediction in East Java, Indonesia. ARIMAX is class of classical time series models that could accurately handle linear pattern and calendar variation effect. Whereas, DNN is known as a machine learning method that powerful to tackle a nonlinear pat

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Rani, K. Chandana, and Y. Prasanth. "A Decision System for Predicting Diabetes using Neural Networks." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 2 (2017): 56. http://dx.doi.org/10.11591/ijai.v6.i2.pp56-65.

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Diabetic retinopathy (DR) is an eye fixed ill complete by the impairment of polygenic disorder and that we purchased to acknowledge it before of calendar for sensible treatment. On these lines, 2 social occasions were perceived, specifically non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR). During this paper, to dissect diabetic retinopathy, 3 models like Probabilistic Neural framework (PNN), Bayesian Classification and Support vector machine (SVM) square measure pictured and their displays square measure thought-about. The live of the unwellness unfold w

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K., Chandana Rani, and Prasanth Y. "A Decision System for Predicting Diabetes using Neural Networks." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 2 (2017): 56–65. https://doi.org/10.5281/zenodo.4108254.

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Diabetic retinopathy (DR) is an eye fixed ill complete by the impairment of polygenic disorder and that we purchased to acknowledge it before of calendar for sensible treatment. On these lines, 2 social occasions were perceived, specifically non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR). During this paper, to dissect diabetic retinopathy, 3 models like Probabilistic Neural framework (PNN), Bayesian Classification and Support vector machine (SVM) square measure pictured and their displays square measure thought-about. The live of the unwellness unfold w

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20

Van Kriekinge, Gilles, Cedric De Cauwer, Nikolaos Sapountzoglou, Thierry Coosemans, and Maarten Messagie. "Day-Ahead Forecast of Electric Vehicle Charging Demand with Deep Neural Networks." World Electric Vehicle Journal 12, no. 4 (2021): 178. http://dx.doi.org/10.3390/wevj12040178.

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The increasing penetration rate of electric vehicles, associated with a growing charging demand, could induce a negative impact on the electric grid, such as higher peak power demand. To support the electric grid, and to anticipate those peaks, a growing interest exists for forecasting the day-ahead charging demand of electric vehicles. This paper proposes the enhancement of a state-of-the-art deep neural network to forecast the day-ahead charging demand of electric vehicles with a time resolution of 15 min. In particular, new features have been added on the neural network in order to improve

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Dubischar-Krivec, Anna Milena, Sven Bölte, Christoph Braun, Fritz Poustka, Niels Birbaumer, and Nicola Neumann. "Neural mechanisms of savant calendar calculating in autism: An MEG-study of few single cases." Brain and Cognition 90 (October 2014): 157–64. http://dx.doi.org/10.1016/j.bandc.2014.07.003.

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22

Celen, Burak, Melik Bugra Ozcelik, Furkan Metin Turgut, et al. "Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries." Open Research Europe 2 (August 12, 2022): 96. http://dx.doi.org/10.12688/openreseurope.14745.1.

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Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxi

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Celen, Burak, Melik Bugra Ozcelik, Furkan Metin Turgut, et al. "Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries." Open Research Europe 2 (February 22, 2023): 96. http://dx.doi.org/10.12688/openreseurope.14745.2.

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Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxi

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Cholissodin, Imam, and Sutrisno Sutrisno. "Prediction of Rainfall using Simplified Deep Learning based Extreme Learning Machines." Journal of Information Technology and Computer Science 3, no. 2 (2018): 120. http://dx.doi.org/10.25126/jitecs.20183258.

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Prediction of rainfall is needed by every farmer to determine the planting period or for an institution, eg agriculture ministry in the form of plant calendars. BMKG is one of the national agency in Indonesia that doing research in the field of meteorology, climatology, and geophysics in Indonesia using several methods in predicting rainfall. However, the accuracy of predicted results from BMKG methods is still less than optimal, causing the accuracy of the planting calendar to only reach 50% for the entire territory of Indonesia. The reason is because of the dynamics of atmospheric patterns (

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Eisinga, Rob, Philip Hans Franses, and Dick van Dijk. "Timing of Vote Decision in First and Second Order Dutch Elections 1978–1995: Evidence from Artificial Neural Networks." Political Analysis 7 (1998): 117–42. http://dx.doi.org/10.1093/pan/7.1.117.

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A time series (t = 921) of weekly survey data on vote intentions in the Netherlands for the period 1978–1995 shows that the percentage of undecided voters follows a cyclical pattern over the election calendar. The otherwise substantial percentage of undecided voters decreases sharply in weeks leading up to an election and gradually increases afterwards. This article models the dynamics of this asymmetric electoral cycle using artificial neural networks, with the purpose of estimating when the undecided voters start making up their minds. We find that they begin to decide which party to vote fo

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Kondath, Namitha, Aung Myat, Yong Loke Soh, Whye Loon Tung, Khoo Aik Min Eugene, and Hui An. "Enhancing Day-Ahead Cooling Load Prediction in Tropical Commercial Buildings Using Advanced Deep Learning Models: A Case Study in Singapore." Buildings 14, no. 2 (2024): 397. http://dx.doi.org/10.3390/buildings14020397.

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Commercial buildings in hot and humid tropical climates rely significantly on cooling systems to maintain optimal occupant comfort. A well-accurate day-ahead load profile prediction plays a pivotal role in planning the energy requirements of cooling systems. Despite the pressing need for effective day-ahead cooling load predictions, current methodologies have not fully harnessed the potential of advanced deep-learning techniques. This paper aims to address this gap by investigating the application of innovative deep-learning models in day-ahead hourly cooling load prediction for commercial bui

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Mayborodin, Alexandr B., Olesya D. Lominago, and Victor A. Vasiliev. "EVALUATION OF CALENDAR AND NETWORK PLANNING QUALITY IN ARTIFICIAL NEURAL NETWORKS APPLICATION IN PROJECT MANAGEMENT INFORMATION SYSTEMS." Ideas and Innovations 8, no. 3-4 (2020): 172–79. http://dx.doi.org/10.48023/2411-7943_2020_8_3_4_172.

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Sabeghi, Rassoul, Karl-Jürgen Bär, and Andy Schumann. "Estimation of calendar age based on autonomic cardiovascular function by applying machine learning techniques." Current Directions in Biomedical Engineering 7, no. 2 (2021): 696–99. http://dx.doi.org/10.1515/cdbme-2021-2177.

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Abstract Aging is accompanied by changes in the cardiovascular physiology that promote the development of age-related diseases. This paper presents a modern approach to quantify the physiological effects of age on the cardiovascular system by applying modern machine learning techniques to several indicators of autonomic cardiovascular function. In 885 healthy subjects, 33 different indices were calculated on resting state electrocardiogram and continuous blood pressure recordings. Based on those parameters, five different approaches were applied in order to reconstruct the calendar age of heal

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Chen, Wenhao, Guangjie Han, Hongbo Zhu, and Lyuchao Liao. "Short-Term Load Forecasting with an Ensemble Model Using Densely Residual Block and Bi-LSTM Based on the Attention Mechanism." Sustainability 14, no. 24 (2022): 16433. http://dx.doi.org/10.3390/su142416433.

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Short-term load forecasting (STLF) is essential for urban sustainable development. It can further contribute to the stable operation of the smart grid. With the development of renewable energy, improving STLF accuracy has become a vital task. Nevertheless, most models based on the convolutional neural network (CNN) cannot effectively extract the crucial features from input data. The reason is that the fundamental requirement of adopting the convolutional neural network (CNN) is space invariance, which cannot be satisfied by the received data, limiting the forecasting performance. Thus, this pa

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Wang, Yang, Hassan A. Karimi, and Xiaowei Jia. "Reconstruction of Continuous High-Resolution Sea Surface Temperature Data Using Time-Aware Implicit Neural Representation." Remote Sensing 15, no. 24 (2023): 5646. http://dx.doi.org/10.3390/rs15245646.

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Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño–Southern Oscillation (ENSO) is considered a significant signal of the global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information (T_INRI) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. T

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Grabarczyk, Sławomir. "Modeling of heat consumption in a greenhouse using experimental data." E3S Web of Conferences 49 (2018): 00037. http://dx.doi.org/10.1051/e3sconf/20184900037.

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The use of experimental research results to teach artificial neural networks was aimed at determining the relationship between heat consumption and measured variables. The mechanism of changes in heat consumption was determined by changes in external climate parameters, microclimate conditions in the greenhouse and parameters describing the functioning of the technical equipment of the facility. The accuracy of modeling the heat consumption in the case of changes in the properties of the building's external partitions has been determined. In a greenhouse, this is related to the functioning of

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S. R, Prof Shegar, Sakshi Rokade, Yash Thikekar, and Prajakta Raut. "Survey Towards Android Application for Plant Disease Detection using Deep Learning Approach." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26776.

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India is a vast country where agriculture is primary occupation for people. Agriculture produce depends on crop and crop yield produced from farming. Crop yield majorly depends on growth and quality of plants. The plants need to be disease free to have good produce from farming. Taking same into consideration the system is designed to detect the plant disease in plants. The android application is created which is integrated with ai- chat and pesticide recommendation for farmer. The convolutional neural network is used for classification of plant disease. The application helps to recommend the

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Cui, Yu, Zishang Zhu, Xudong Zhao, and Zhaomeng Li. "Energy Schedule Setting Based on Clustering Algorithm and Pattern Recognition for Non-Residential Buildings Electricity Energy Consumption." Sustainability 15, no. 11 (2023): 8750. http://dx.doi.org/10.3390/su15118750.

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Building energy modelling (BEM) is crucial for achieving energy conservation in buildings, but occupant energy-related behaviour is often oversimplified in traditional engineering simulation methods and thus causes a significant deviation between energy prediction and actual consumption. Moreover, the conventional fixed schedule-setting method is not applicable to the recently developed data-driven BEM which requires a more flexible and data-related multi-timescales schedule-setting method to boost its performance. In this paper, a data-based schedule setting method is developed by applying K-

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Caicedo-Vivas, Joan Sebastian, and Wilfredo Alfonso-Morales. "Short-Term Load Forecasting Using an LSTM Neural Network for a Grid Operator." Energies 16, no. 23 (2023): 7878. http://dx.doi.org/10.3390/en16237878.

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Electricity is crucial for daily life due to the number of activities that depend on it. To forecast future electric load, which changes over time and depends on various factors, grid operators (GOs) must create forecasting models for various time horizons with a high degree of accuracy because the results have a huge impact on their decision-making regarding, for example, the scheduling of power units to supply user consumption in the short or long term or the installation of new power plants. This has led to the exploration of multiple techniques like statistical models and Artificial Intell

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Ećim-Đurić, Olivera, Mihailo Milanović, Aleksandra Dimitrijević-Petrović, Zoran Mileusnić, Aleksandra Dragičević, and Rajko Miodragović. "Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks." Agronomy 14, no. 6 (2024): 1147. http://dx.doi.org/10.3390/agronomy14061147.

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In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately predict and maintain ideal temperature and relative humidity, is crucial for enhancing plant growth and health, optimizing resource use, and ensuring sustainable agricultural practices. However, maintaining optimal microclimatic conditions is a significant challenge due to the dyna

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Cebeci, Cagatay, and Kasım Zor. "Electricity Demand Forecasting Using Deep Polynomial Neural Networks and Gene Expression Programming During COVID-19 Pandemic." Applied Sciences 15, no. 5 (2025): 2843. https://doi.org/10.3390/app15052843.

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The power-generation mix of future grids will be quite diversified with the ever-increasing share of renewable energy technologies. Therefore, the prediction of electricity demand will become crucial for resource optimization and grid stability. Machine learning- and artificial intelligence-based methods are widely studied by researchers to tackle the demand forecasting problem. However, since the COVID-19 pandemic broke out, new challenges have surfaced for forecasting research. In such a short amount of time, significant shifts have emerged in electricity demand trends, making it apparent th

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Wang, H., C. Wang, Y. Zhao, X. Lin, and C. Yu. "Toward a practical approach for ergodicity analysis." Nonlinear Processes in Geophysics Discussions 2, no. 5 (2015): 1425–46. http://dx.doi.org/10.5194/npgd-2-1425-2015.

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Abstract. It is of importance to perform hydrological forecast using a finite hydrological time series. Most time series analysis approaches presume a data series to be ergodic without justifying this assumption. This paper presents a practical approach to analyze the mean ergodic property of hydrological processes by means of autocorrelation function evaluation and Augmented Dickey Fuller test, a radial basis function neural network, and the definition of mean ergodicity. The mean ergodicity of precipitation processes at the Lanzhou Rain Gauge Station in the Yellow River basin, the Ankang Rai

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Xie, Yuhong, Yuzuru Ueda, and Masakazu Sugiyama. "A Two-Stage Short-Term Load Forecasting Method Using Long Short-Term Memory and Multilayer Perceptron." Energies 14, no. 18 (2021): 5873. http://dx.doi.org/10.3390/en14185873.

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Load forecasting is an essential task in the operation management of a power system. Electric power companies utilize short-term load forecasting (STLF) technology to make reasonable power generation plans. A forecasting model with low prediction errors helps reduce operating costs and risks for the operators. In recent years, machine learning has become one of the most popular technologies for load forecasting. In this paper, a two-stage STLF model based on long short-term memory (LSTM) and multilayer perceptron (MLP), which improves the forecasting accuracy over the entire time horizon, is p

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Xia, Zetao, Yining Wang, Longhua Ma, et al. "A Hybrid Prognostic Method for Proton-Exchange-Membrane Fuel Cell with Decomposition Forecasting Framework Based on AEKF and LSTM." Sensors 23, no. 1 (2022): 166. http://dx.doi.org/10.3390/s23010166.

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Durability and reliability are the major bottlenecks of the proton-exchange-membrane fuel cell (PEMFC) for large-scale commercial deployment. With the help of prognostic approaches, we can reduce its maintenance cost and maximize its lifetime. This paper proposes a hybrid prognostic method for PEMFCs based on a decomposition forecasting framework. Firstly, the original voltage data is decomposed into the calendar aging part and the reversible aging part based on locally weighted regression (LOESS). Then, we apply an adaptive extended Kalman filter (AEKF) and long short-term memory (LSTM) neura

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Borges, Pedro Hurtado de Mendoza, Zaíra Morais dos Santos Hurtado De Mendoza, and Pedro Hurtado de Mendoza Morais. "Previsión del ambiente térmico para el ganado lechero mediante redes neuronales artificiales." South Florida Journal of Environmental and Animal Science 1, no. 4 (2021): 104–19. http://dx.doi.org/10.53499/sfjeasv1n4-001.

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El presente estudio tuvo como objetivo la previsión del ambiente térmico para el ganado lechero mediante redes neuronales artificiales, de acordó con la temperatura y humedad diaria. En la investigación se utilizaron los valores diarios de esas variables, disponibles en el Instituto Nacional de Meteorología de Brasil. Los datos correspondieron a las series históricas registradas en estaciones convencionales con tiempo de operación superior a 30 años hasta 2020. A continuación, se seleccionaron los municipios Canarana, Matupá, Nova Xavantina y Santo Antônio de Leverger, localizados en Mato Gros

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Borges, Pedro Hurtado de Mendoza, Zaíra Morais dos Santos Hurtado De Mendoza, and Pedro Hurtado de Mendoza Morais. "Previsión del ambiente térmico para el ganado lechero mediante redes neuronales artificiales." South Florida Journal of Environmental and Animal Science 1, no. 4 (2021): 104–19. http://dx.doi.org/10.53499/sfjeasv1n4-001.

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El presente estudio tuvo como objetivo la previsión del ambiente térmico para el ganado lechero mediante redes neuronales artificiales, de acordó con la temperatura y humedad diaria. En la investigación se utilizaron los valores diarios de esas variables, disponibles en el Instituto Nacional de Meteorología de Brasil. Los datos correspondieron a las series históricas registradas en estaciones convencionales con tiempo de operación superior a 30 años hasta 2020. A continuación, se seleccionaron los municipios Canarana, Matupá, Nova Xavantina y Santo Antônio de Leverger, localizados en Mato Gros

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Borges, Pedro Hurtado de Mendoza, Zaíra Morais dos Santos Hurtado de Mendoza, and Pedro Hurtado de Mendoza Morais. "PRONÓSTICO ANUAL DE LA CARGA TÉRMICA RADIANTE APLICÁNDOSE INTELIGENCIA ARTIFICIAL." Nativa 9, no. 3 (2021): 229–35. http://dx.doi.org/10.31413/nativa.v9i3.10122.

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En este estudio se desenvolvieron redes neuronales artificiales para predecir el conforto térmico animal, en función de la temperatura ambiente y la velocidad del aire para cada día del año en el calendario juliano. Los datos fueron obtenidos en el sitio del Instituto Nacional de Meteorología para una serie histórica de 30 años, coleccionada en la Estación Convencional Padre Ricardo Remetter, municipio de Santo Antonio de Leverger-MT. Para la elaboración de las redes se adoptó como variable de entrada el día del año y como variable de salida la carga térmica de radiación. El número de neuronas

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Karim, Abdul, and Abdul Rasheed. "Forecasting Modeling of Day of the Week Calendar Anomalies in Pakistan Stock Exchange: An Artificial Intelligence Perspective." Bulletin of Business and Economics (BBE) 13, no. 2 (2024): 436–47. http://dx.doi.org/10.61506/01.00351.

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Stock price forecasting provide valuable insight to the investor to facilitate well-informed investment decision making. The aim of this study is to examine the calendar anomalies i.e. DOW in Pakistan stock exchange though Artificial intelligence techniques. For this purpose, Support vector machine (SVM), Decision Tree (DT) and Artificial Neural Network is used to forecast the daily stock prices. The daily stock prices data of KSE100 index ranges from May,1994 to August 2023 is used as out variable while stock open, close, high and low prices are used as features/input variables. The training

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Wang, Ziyang, Masahiro Mae, Takeshi Yamane, Masato Ajisaka, Tatsuya Nakata, and Ryuji Matsuhashi. "Enhanced Day-Ahead Electricity Price Forecasting Using a Convolutional Neural Network–Long Short-Term Memory Ensemble Learning Approach with Multimodal Data Integration." Energies 17, no. 11 (2024): 2687. http://dx.doi.org/10.3390/en17112687.

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Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to nearly zero JPY/kWh during peak RES production periods, substantially affecting transactions between electricity retailers and consumers. This paper introduces an innovative DAEPF framework employing a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to predict day-ahea

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Harteveld, Dalphy O. C., Michael R. Grant, Jay W. Pscheidt, and Tobin L. Peever. "Predicting Ascospore Release of Monilinia vaccinii-corymbosi of Blueberry with Machine Learning." Phytopathology® 107, no. 11 (2017): 1364–71. http://dx.doi.org/10.1094/phyto-04-17-0162-r.

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Mummy berry, caused by Monilinia vaccinii-corymbosi, causes economic losses of highbush blueberry in the U.S. Pacific Northwest (PNW). Apothecia develop from mummified berries overwintering on soil surfaces and produce ascospores that infect tissue emerging from floral and vegetative buds. Disease control currently relies on fungicides applied on a calendar basis rather than inoculum availability. To establish a prediction model for ascospore release, apothecial development was tracked in three fields, one in western Oregon and two in northwestern Washington in 2015 and 2016. Air and soil temp

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САВВИН, Н. В., Д. Н. ВАСЕНИН та Д. С. СВИРИДОВ. "ОБОСНОВАНИЕ МЕТОДА ОБРАБОТКИ ИНФОРМАЦИИ ДЛЯ ПОВЫШЕНИЯ ТОЧНОСТИ КРАТКОСРОЧНОГО ПРОГНОЗА ЭЛЕКТРОПОТРЕБЛЕНИЯ (НА ПРИМЕРЕ КОМПЛЕКСА ОБЪЕКТОВ ИНЖЕНЕРНОГО КАМПУСА УНИВЕРСИТЕТА)". Инженерные системы и сооружения, № 1(59) (4 квітня 2025): 149–54. https://doi.org/10.36622/2074-188x.2025.36.19.014.

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В данной статье представлен новый метод краткосрочного прогнозирования электрической нагрузки, в котором акцент делается на интеграцию календарных данных и уникального метода временного кодирования. Проведённый анализ показал, что погодные переменные оказывают незначительное влияние на точность прогнозов. В связи с этим предложен новый подход, позволяющий моделям лучше понимать временные закономерности, используя синусоидальные и косинусоидальные преобразования минут, часов, дней недели и года. Для прогнозирования нагрузки применялись модели машинного обучения: LSTM (долгая краткосрочная памят

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Yohanani, Efi, Amit Frisch, Victor Lukyanov, Shabtai Cohen, Meir Teitel, and Josef Tanny. "Estimating Evapotranspiration of Screenhouse Banana Plantations Using Artificial Neural Network and Multiple Linear Regression Models." Water 14, no. 7 (2022): 1130. http://dx.doi.org/10.3390/w14071130.

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Measured evapotranspiration (LE) of screenhouse banana plantations was utilized to derive and compare two types of machine-learning models: artificial neural network (ANN) and multiple linear regression (MLR). The measurements were conducted by eddy-covariance systems and meteorological sensors in two similar screenhouse banana plantations during two consecutive seasons, 2016 and 2017. Most of the study focused on the season of 2017, which includes a more extended data set (141 days) than 2016 (52 days). The results show that in most cases, the ANN model was superior to MLR. When trained and v

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Truong, Robert, Olga Gkountouna, Dieter Pfoser, and Andreas Züfle. "Towards a Better Understanding of Public Transportation Traffic: A Case Study of the Washington, DC Metro." Urban Science 2, no. 3 (2018): 65. http://dx.doi.org/10.3390/urbansci2030065.

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The problem of traffic prediction is paramount in a plethora of applications, ranging from individual trip planning to urban planning. Existing work mainly focuses on traffic prediction on road networks. Yet, public transportation contributes a significant portion to overall human mobility and passenger volume. For example, the Washington, DC metro has on average 600,000 passengers on a weekday. In this work, we address the problem of modeling, classifying and predicting such passenger volume in public transportation systems. We study the case of the Washington, DC metro exploring fare card da

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Kiprijanovska, Ivana, Simon Stankoski, Igor Ilievski, Slobodan Jovanovski, Matjaž Gams, and Hristijan Gjoreski. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning." Energies 13, no. 10 (2020): 2672. http://dx.doi.org/10.3390/en13102672.

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Short-term load forecasting is integral to the energy planning sector. Various techniques have been employed to achieve effective operation of power systems and efficient market management. We present a scalable system for day-ahead household electrical energy consumption forecasting, named HousEEC. The proposed forecasting method is based on a deep residual neural network, and integrates multiple sources of information by extracting features from (i) contextual data (weather, calendar), and (ii) the historical load of the particular household and all households present in the dataset. Additio

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Mujeeb, Sana, Turki Ali Alghamdi, Sameeh Ullah, Aisha Fatima, Nadeem Javaid, and Tanzila Saba. "Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics." Applied Sciences 9, no. 20 (2019): 4417. http://dx.doi.org/10.3390/app9204417.

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Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and econo

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