scholarly journals Case Study

2019 ◽  
pp. 85-96
Author(s):  
Maria Jacob ◽  
Cláudia Neves ◽  
Danica Vukadinović Greetham
Keyword(s):  
Chronological Order ◽  
Short Term ◽  
Short Term Forecasting

Abstract In the previous chapter, we looked at load measurements for all households together and we ignored their chronological order. In contrast, in this chapter, we are interested in short term forecasting of household profiles individually. Therefore, information about the time at which measurements were taken becomes relevant.

Case Study: Analysis and Predictions of Shipment Load in Festive Season

2017 ◽  
Vol 7 (7) ◽  
pp. 491
Author(s):  
Megha Chhabra
Keyword(s):  
Time Series Models ◽  
Multiplicative Decomposition ◽  
Short Term ◽  
Case Study Analysis ◽  
Use Of Time ◽  
Load Demand ◽  
Huge Impact ◽  
Short Term Forecasting ◽  
Festive Season

A time-phased forecasting in rest of the year has a huge impact shipping costs, however during a festive season of the year, well predicted and analyzed re-engineering of shipment load plays a major role in bringing up sales. The major concern of the customer is to get delivery on-time, whereas that of the wholesaler / retailer is to provide delivery without any complaint in order to retain the customer. In the framework of competitive supply chain market, necessary accurate Shipping load forecasting tools are required. With the focus of improving prediction accuracy, this case study presents use of Time-series models, multiplicative decomposition model (MDM) and smoothening techniques, on shipping load demand of Arora-Ludhiana-Handlooms during festive seasons for short-term forecasting.

A New Approach in Short-Term Prediction of the Electrical Charge with Regression Models A Case Study

2013 ◽  
Vol 4 (3) ◽  
pp. 34-46
Author(s):  
Farhad Soleimanian Gharehchopoghi ◽  
Freshte Dabaghchi Mokri ◽  
Maryam Molany
Keyword(s):  
Power Systems ◽  
Distribution Systems ◽  
Short Term ◽  
Forecasting Error ◽  
Load Fluctuation ◽  
Distribution Company ◽  
Short Term Forecasting ◽  
West Azerbaijan ◽  
Short Term Prediction

The accuracy of forecasting of electrical load for the electricity industry has a vital significance in the renewal of economic structure as well as various equations including: purchasing and producing energy, load fluctuation, and the development of infrastructures. Its short-term forecasting has a significant role in designing and utilizing power systems and in the distribution systems and having a variety of systems used to maintain security potentials for the system. In this paper, we attempted to carry out a short-term forecasting of electrical distribution company in west Azerbaijan state in Iran's electricity in a few days on the basis of regression multi linear model. This forecasting which was done during a three-day period is and categorized weekdays into three groups including working days, weekends, and holidays was carried out in an hourly manner. This model regardless of parameters like humidity, wind velocity, daylight time, etc. by minimizing the forecasting error managed to maximize the reliability of the results as well as the safety potential of the system. In this model the only influential parameter on the forecasting was the reliance of the forecasting day on previous days. The main purpose of the present study was to maximize the accuracy and reliability of forecasting for certain days (religious holidays, national holidays …). In this paper, the authors managed to decrease the error of forecasting for particular and regular off days to a great extent.

scholarly journals Multisource Data Framework for Road Traffic State Estimation

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Josep Maria Salanova Grau ◽  
Evangelos Mitsakis ◽  
Panagiotis Tzenos ◽  
Iraklis Stamos ◽  
Luigi Selmi ◽  
...  
Keyword(s):  
Road Traffic ◽  
Road Networks ◽  
Added Value ◽  
Short Term ◽  
The Road ◽  
Data Framework ◽  
Traffic Volumes ◽  
Short Term Forecasting ◽  
The City

This paper presents a framework for data collection, filtering, and fusion, together with a set of operational tools to validate, analyze, utilize, and highlight the added value of probe data. Data is collected by both conventional (loops, radars, and cameras) and innovative (Floating Car Data, detectors of Bluetooth devices) technologies and refers to travel times and traffic flows on road networks. The city of Thessaloniki, Greece, serves as a case study for the implementation of the proposed framework. The methodology includes the estimation of traffic flow based on measured travel time along predefined routes and short-term forecasting of traffic volumes and their spatial expansion in the road network. The proposed processes and the framework itself have the potential of being implemented in urban road networks.

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