Globally local: Hyper-local modeling for accurate forecast of COVID-19

Accurate forecast of aggregate end-users electric load profiles is becoming a hot topic in research for those main issues addressed in many fields such as the electricity services market. Hence, load forecast is an extremely important task which should be understood more in depth. In this research paper, the dependency of the day-ahead load forecast accuracy on the basis of the data typology employed in the training of LSTM has been inspected. A real case study of an Italian industrial load with samples recorded every 15 min for the year 2017 and 2018 was studied. The effect in the load forecast accuracy of different dataset cleaning approaches was investigated. In addition, the Generalised Extreme Studentized Deviate hypothesis testing was introduced to identify the outliers present in the dataset. The populations were constructed on the basis of an autocorrelation analysis that allowed for identifying a weekly correlation of the samples. The accuracy of the prediction obtained from different input dataset has been therefore investigated by calculating the most commonly used error metrics, showing the importance of data processing before employing them for load forecast.

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Trend analysis and forecast of daily reported incidence of hand, foot and mouth disease in Hubei, China by Prophet model

Scientific Reports ◽

10.1038/s41598-021-81100-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Cong Xie ◽

Haoyu Wen ◽

Wenwen Yang ◽

Jing Cai ◽

Peng Zhang ◽

...

Keyword(s):

Moving Average ◽

Arima Model ◽

Foot And Mouth Disease ◽

Mouth Disease ◽

Spring Festival ◽

High Incidence ◽

Foot And Mouth ◽

Accurate Forecast ◽

Changing Patterns ◽

Two Peaks

AbstractHand, foot, and mouth disease (HFMD) is common among children below 5 years. HFMD has a high incidence in Hubei Province, China. In this study, the Prophet model was used to forecast the incidence of HFMD in comparison with the autoregressive-integrated moving average (ARIMA) model, and HFMD incidence was decomposed into trends, yearly, weekly seasonality and holiday effect. The Prophet model fitted better than the ARIMA model in daily reported incidence of HFMD. The HFMD incidence forecast by the Prophet model showed that two peaks occurred in 2019, with the higher peak in May and the lower peak in December. Periodically changing patterns of HFMD incidence were observed after decomposing the time-series into its major components. In specific, multi-year variability of HFMD incidence was found, and the slow-down increasing point of HFMD incidence was identified. Relatively high HFMD incidences appeared in May and on Mondays. The effect of Spring Festival on HFMD incidence was much stronger than that of other holidays. This study showed the potential of the Prophet model to detect seasonality in HFMD incidence. Our next goal is to incorporate climate variables into the Prophet model to produce an accurate forecast of HFMD incidence.

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Local Modeling Algorithm Based on Similarity of Vector

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.182-183.1060 ◽

2012 ◽

Vol 182-183 ◽

pp. 1060-1064

Author(s):

Jing Zeng ◽

Jun Wang ◽

Jin Yu Guo

Keyword(s):

Local Model ◽

Local Models ◽

Local Polynomial Fitting ◽

Local Polynomial ◽

Data Matching ◽

Local Modeling ◽

Fitting Algorithm ◽

System Input ◽

Simulation Results ◽

Historical System

A mutli-model modeling method based on local model is given. The modeling idea is firstly to find some data matching with the current working point from vast historical system input-output datasets, and in this paper, we give a new method of choose data information based on similarity of vector which improve the accuracy of data greatly. Secondly to choose the weight and optimum bandwidth then develop a local model using local polynomial fitting algorithm. With the change of working points, multiple local models are built. The effectiveness of the proposed method is demonstrated by simulation results.

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Affine Covariant Features for Fisheye Distortion Local Modeling

IEEE Transactions on Image Processing ◽

10.1109/tip.2016.2627816 ◽

2017 ◽

Vol 26 (2) ◽

pp. 696-710 ◽

Cited By ~ 9

Author(s):

Antonino Furnari ◽

Giovanni Maria Farinella ◽

Arcangelo Ranieri Bruna ◽

Sebastiano Battiato

Keyword(s):

Local Modeling

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Non-local modeling of thermal shock damage in refractory materials

Engineering Fracture Mechanics ◽

10.1016/j.engfracmech.2008.06.031 ◽

2008 ◽

Vol 75 (16) ◽

pp. 4706-4720 ◽

Cited By ~ 12

Author(s):

F. Damhof ◽

W.A.M. Brekelmans ◽

M.G.D. Geers

Keyword(s):

Thermal Shock ◽

Refractory Materials ◽

Local Modeling ◽

Non Local

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Optimization of a new thermoelectric cooling assembly using CFD analysis and local modeling of thermoelectric effects

Proceedings ICT'03. 22nd International Conference on Thermoelectrics (IEEE Cat. No.03TH8726) ◽

10.1109/ict.2003.1287588 ◽

2004 ◽

Author(s):

M.P. Codecasa ◽

E. Colombo ◽

F. Inzoli ◽

G. Pastorino ◽

C. Rizzo

Keyword(s):

Cfd Analysis ◽

Thermoelectric Cooling ◽

Thermoelectric Effects ◽

Local Modeling

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A Research on Markov Model and Hidden Markov Model in Crude Oil Price Forecasting for PETRONAS Malaysia

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l1023.10812s219 ◽

2019 ◽

Vol 8 (12S2) ◽

pp. 121-125 ◽

Cited By ~ 1

Keyword(s):

Markov Model ◽

Crude Oil ◽

Hidden Markov Model ◽

Structural Equation ◽

Hidden Markov ◽

Oil Price ◽

Price Forecasting ◽

Crude Oil Price ◽

Crude Oil Prices ◽

Accurate Forecast

Crude oil price forecasting is an essential component of sustainable development of many countries as crude oil is an unavoidable product that exists on earth. In this paper, a model based on a hidden Markov model and Markov model for crude oil price forecasting was developed, and their relative performance was compared. Path analysis of Structural Equation Modelling was employed to model the effects of forecasted prices and the actual crude oil price to get the most accurate forecast. The key variables used to develop the models were monthly crude oil prices s from PETRONAS Malaysia. It was found that the hidden Markov model was more accurate than the Markov model in forecasting the crude oil price. The findings of this study show that the hidden Markov model is a potentially promising method of crude oil price forecasting that merit further study.

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Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production

MATEMATIKA ◽

10.11113/matematika.v34.n1.1040 ◽

2018 ◽

Vol 34 (1) ◽

pp. 103-111 ◽

Cited By ~ 4

Author(s):

Suhartono Suhartono ◽

Dedy Dwi Prastyo ◽

Heri Kuswanto ◽

Muhammad Hisyam Lee

Keyword(s):

Forecast Accuracy ◽

Oil Production ◽

Training Data ◽

Temporal Data ◽

Monthly Data ◽

Vector Autoregressive ◽

Temporal Models ◽

Accurate Forecast ◽

Temporal Model ◽

Spatio Temporal

Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal models are proposed and applied for forecasting monthly oil production data at three drilling wells in East Java, Indonesia. There are 60 observations that be divided to two parts, i.e. the first 50 observations for training data and the last 10 observations for testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11) as linear spatio-temporal models. Moreover, further research about nonlinear spatio-temporal models based on neural networks and GSTAR is needed for developing new hybrid models that could improve the forecast accuracy.

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Validation of BeiDou Observations

Journal of Applied Geodesy ◽

10.1515/jag-2013-0027 ◽

2014 ◽

Vol 8 (2) ◽

Cited By ~ 1

Author(s):

Ahmed El-Mowafy ◽

Congwei Hu

Keyword(s):

Single Point ◽

Real Data ◽

Empirical Method ◽

Observation Model ◽

Triple Frequency ◽

Local Modeling ◽

Detection And Identification ◽

Using Data ◽

Navigation Information ◽

Graphical Diagnostics

AbstractThis study presents validation of BeiDou measurements in un-differenced standalone mode and experimental results of its application for real data. A reparameterized form of the unknowns in a geometry-free observation model was used. Observations from each satellite are independently screened using a local modeling approach. Main advantages include that there is no need for computation of inter-system biases and no satellite navigation information are needed. Validation of the triple-frequency BeiDou data was performed in static and kinematic modes, the former at two continuously operating reference stations in Australia using data that span two consecutive days and the later in a walking mode for three hours. The use of the validation method parameters for numerical and graphical diagnostics of the multi-frequency BeiDou observations are discussed. The precision of the system’s observations was estimated using an empirical method that utilizes the characteristics of the validation statistics. The capability of the proposed method is demonstrated in detection and identification of artificial errors inserted in the static BeiDou data and when implemented in a single point positioning processing of the kinematic test.

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Improved Site-Specific Numerical Prediction of Fog and Low Clouds: A Feasibility Study

Weather and Forecasting ◽

10.1175/waf873.1 ◽

2005 ◽

Vol 20 (4) ◽

pp. 627-646 ◽

Cited By ~ 73

Author(s):

Thierry Bergot ◽

Dominique Carrer ◽

Joël Noilhan ◽

Philippe Bougeault

Keyword(s):

Winter Season ◽

Integrated Approach ◽

Atmospheric Measurements ◽

Forecast System ◽

Short Term ◽

Low Clouds ◽

Atmospheric Profiles ◽

Low Cloud ◽

Accurate Forecast ◽

1D Model

Abstract Accurate short-term forecasts of low ceiling and visibility are vital to air traffic operation, in order to maximize the use of an airport. The research presented here uses specific local observations and a detailed numerical 1D model in an integrated approach. The goal of the proposed methodology is to improve the local prediction of poor visibility and low clouds at Paris’s Charles de Gaulle International Airport. In addition to the development of an integrated observations and model-based forecasting system, this study will try to assess whether or not the increased local observing network yields improvements in short-term forecasts of low ceiling and poor visibility. Tests have been performed in a systematic manner during 5 months (the 2002/03 winter season). Encouraging results show that the inclusion of dedicated observations into the local 1D forecast system provides significant improvement to the forecast. Inspection of events indicates that the improvement in very short-term forecasts is a consequence of the ability of the forecast system to more accurately characterize the boundary layer processes, especially during night. Accurate forecast of low cloud seems more difficult since it strongly depends on the 3D mesoscale flow. This study also demonstrates that the use of a 1D model to forecast fogs and low clouds could only be beneficial if it is associated with local measurements and with a local assimilation scheme. The assimilation procedure used in this study is based on different steps: in the first step the atmospheric profiles are estimated in a one-dimensional variational data assimilation (1DVAR) framework, in the second step these atmospheric profiles are modified when fog and/or low clouds are detected, and in the third step the soil profiles are estimated in order to keep the consistency between the soil state and atmospheric measurements.

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