scholarly journals A Fault Forecast Method for Refrigerated Super-Showcases Based on Operation Data. 1st Report.

2001 ◽  
Vol 67 (655) ◽  
pp. 744-750
Author(s):  
Tsuyoshi KAWAI ◽  
Kazuaki MIZUKAMI ◽  
Yoshio OZAWA ◽  
Matatugu HONJO ◽  
Satoshi OHYA ◽  
...  
Keyword(s):  
Forecast Method

A Novel Forecast Method for Air-Conditioning Load of Public Building Considering Accumulated Temperature Effect

2015 ◽  
Vol 9 (1) ◽  
pp. 363-367
Author(s):  
Qingshan Xu ◽  
Xufang Wang ◽  
Chenxing Yang ◽  
Hong Zhu ◽  
Qingguo Yan
Keyword(s):  
Neural Network ◽  
Temperature Effect ◽  
Air Conditioning ◽  
Forecast Accuracy ◽  
Power Network ◽  
Forecast Method ◽  
Elman Neural Network ◽  
Public Building ◽  
Effective Accumulated Temperature ◽  
Accumulated Temperature

It has great significance to estimate the schedulable capacity of air-conditioning load of public building for participating the power network regulation by forecasting the air-conditioning load accurately. A novel forecast method considering the accumulated temperature effect is proposed in this paper based on Elman neural network. Firstly, the starting and ending date for forecast considering the accumulated temperature effect are determined by providing the five day sliding average thermometer algorithm which is usually adopted in aerology research. Then, the effective accumulated temperature of each day is calculated. Finally, take the effective accumulated temperature, temperature and humidity into consideration, the air-conditioning load of public building in the forecast day is acquired by Elman neural network. Simulated results show that the higher forecast accuracy can be achieved by considering the accumulated temperature effect.

Research of Short-Term Traffic Flow Forecast Method Based on the Kalman Filter

ICCTP 2011 ◽  
2011 ◽  
Author(s):  
Feng Chen ◽  
Yuanhua Jia ◽  
Wenjuan An ◽  
Na Zhang ◽  
Zhonghai Niu
Keyword(s):  
Kalman Filter ◽  
Traffic Flow ◽  
Forecast Method ◽  
Short Term ◽  
Traffic Flow Forecast ◽  
Flow Forecast

scholarly journals A revised forecast method of ECMWF precipitation based on CNN feature extraction

2021 ◽  
Vol 658 (1) ◽  
pp. 012025
Author(s):  
Huasheng Zhao ◽  
Xiaoyan Huang ◽  
Ying Huang ◽  
Jian Jin
Keyword(s):  
Feature Extraction ◽  
Forecast Method

Evaluation of Statistical Forecast Method Efficiency in the Conditions of Dynamic Chaos

2021 ◽  
Author(s):  
Rafael' M. Yusupov ◽  
Aleksandr A. Musaev ◽  
Dmitrij A. Grigoriev
Keyword(s):  
Dynamic Chaos ◽  
Forecast Method

scholarly journals Load forecast method of electric vehicle charging station using SVR based on GA-PSO

2017 ◽  
Vol 69 ◽  
pp. 012196 ◽  
Author(s):  
Kuan Lu ◽  
Wenxue Sun ◽  
Changhui Ma ◽  
Shenquan Yang ◽  
Zijian Zhu ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Forecast Method ◽  
Load Forecast ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

scholarly journals Coronavirus (COVID-19) Pandemic in Indonesia: Cases Overview and Daily Data Time Series using Naïve Forecast Method

2021 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Annisa Puspa Kirana ◽  
Adhitya Bhawiyuga
Keyword(s):  
Time Series ◽  
Percentage Error ◽  
Forecast Method ◽  
Monthly Basis ◽  
Average Case ◽  
Monthly Average ◽  
Daily Data ◽  
Time Series Dataset ◽  
The World ◽  
Forecasting Analysis

At the end of December 2019, the virus emerges from Wuhan, China, and resulted in a severe outbreak in many cities in China and expanding globally, including Indonesia. Indonesia is the fourth most populated country globally. As of February 2021, Indonesia in the first rank of positive cases of COVID-19 in Southeast Asia, number 4 in Asia, and number 19 in the world. Our paper aims to provide detailed reporting and analysis of the COVID-19 case overview and forecasting that have hit Indonesia. Our time-series dataset from March 2020 to January 2021. Summary of cases studied included the number of positive cases and deaths due to COVID-19 on a daily or monthly basis. We use time series and forecasting analysis using the Naïve Forecast method.  The prediction is daily case prediction for six months starting from February 1, 2021, to June 30, 2021, using active cases daily COVID-19 data in all provinces in Indonesia. The highest monthly average case prediction is in June, which is 35,662 cases. Our COVID-19 prediction study has a mean absolute percentage error (MAPE) score of 15.85%.

scholarly journals Short-Term Master-Slave Forecast Method for Distributed Photovoltaic Plants Based on the Spatial Correlation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jia Ning ◽  
Guanghao Lu ◽  
Sipeng Hao ◽  
Aidong Zeng ◽  
Hualei Wang
Keyword(s):  
Power Systems ◽  
Spatial Correlation ◽  
Data Storage ◽  
Power Plants ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Storage Condition ◽  
Support Vector ◽  
Forecast Method ◽  
Short Term

With the large-scale integration of distributed photovoltaic (DPV) power plants, the uncertainty of photovoltaic generation is intensively influencing the secure operation of power systems. Improving the forecast capability of DPV plants has become an urgent problem to solve. However, most of the DPV plants are not able to make generation forecast on their own due to the constraints of the investment cost, data storage condition, and the influence of microscope environment. Therefore, this paper proposes a master-slave forecast method to predict the power of target plants without forecast ability based on the power of DPV plants with comprehensive forecast system and the spatial correlation between these two kinds of plants. First, a characteristics pattern library of DPV plants is established with K-means clustering algorithm considering the time difference. Next, the pattern most spatially correlated to the target plant is determined through online matching. The corresponding spatial correlation mapping relationship is obtained by numerical fitting using least squares support vector machine (LS-SVM), and the short-term generation forecast for target plants is achieved with the forecast of reference plants and mapping relationship. Simulation results demonstrate that the proposed method could improve the overall forecast accuracy by more than 52% for univariate prediction and by more than 22% for multivariate prediction and obtain short-term generation forecast for DPV or newly built DPV plants with low investment.

Statistical Tropical Cyclone Wind Radii Prediction Using Climatology and Persistence

2007 ◽  
Vol 22 (4) ◽  
pp. 781-791 ◽  
Author(s):  
John A. Knaff ◽  
Charles R. Sampson ◽  
Mark DeMaria ◽  
Timothy P. Marchok ◽  
James M. Gross ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Department Of Defense ◽  
Parametric Model ◽  
Forecast Method ◽  
Operational Model ◽  
Wind Structure ◽  
East Pacific ◽  
Operational Forecasts ◽  
National Hurricane Center ◽  
Joint Typhoon Warning Center

Abstract An operational model used to predict tropical cyclone wind structure in terms of significant wind radii (i.e., 34-, 50-, and 64-kt wind radii, where 1 kt = 0.52 m s−1) at the National Oceanic and Atmospheric Administration/National Hurricane Center (NHC) and the Department of Defense/Joint Typhoon Warning Center (JTWC) is described. The statistical-parametric model employs aspects of climatology and persistence to forecast tropical cyclone wind radii through 5 days. Separate versions of the model are created for the Atlantic, east Pacific, and western North Pacific by statistically fitting a modified Rankine vortex, which is generalized to allow wavenumber-1 asymmetries, to observed values of tropical cyclone wind radii as reported by NHC and JTWC. Descriptions of the developmental data and methods used to formulate the model are given. A 2-yr verification and comparison with operational forecasts and an independently developed wind radii forecast method that also employs climatology and persistence suggests that the statistical-parametric model does a good job of forecasting wind radii. The statistical-parametric model also provides reliable operational forecasts that serve as a baseline for evaluating the skill of operational forecasts and other wind radii forecast methods in these tropical cyclone basins.

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