The Wharton Long Term Model: Input-Output within the Context of a Macro Forecasting Model

1975 ◽  
Vol 16 (1) ◽  
pp. 3 ◽  
Author(s):  
R. S. Preston
Keyword(s):  
Forecasting Model ◽  
Input Output ◽  
Model Input ◽  
Term Model

Selective arterialization of V.cordis magna (retrobypass) prevents hemodynamic instability after LAD-occlusion in a porcine long term model

2009 ◽  
Vol 56 (S 01) ◽  
Author(s):  
F Harig ◽  
J Schmidt ◽  
E Hoyer ◽  
D Labahn ◽  
K Amann ◽  
...  
Keyword(s):  
Hemodynamic Instability ◽  
Term Model

scholarly journals Evaluating long-term model-based scenarios of the energy system

2020 ◽  
Vol 32 ◽  
pp. 100551
Author(s):  
Matthew Binsted ◽  
Gokul Iyer ◽  
Ryna Cui ◽  
Zarrar Khan ◽  
Kalyn Dorheim ◽  
...  
Keyword(s):  
Energy System ◽  
Model Based ◽  
Term Model

scholarly journals Deteriorating beta‐cell function in type 2 diabetes: a long‐term model

QJM ◽  
2003 ◽  
Vol 96 (4) ◽  
pp. 281-288 ◽  
Author(s):  
A. Bagust ◽  
S. Beale
Keyword(s):  
Type 2 Diabetes ◽  
Beta Cell ◽  
Beta Cell Function ◽  
Cell Function ◽  
Term Model

scholarly journals LONG TERM PERFORMANCE PREDICTION FOR DHW SOLAR SYSTEMS BASED ON INPUT-OUTPUT TEST METHOD

1990 ◽  
pp. 487-491
Author(s):  
B. Bourges ◽  
A. Rabl ◽  
B. Leide ◽  
M.J. Carvalho ◽  
M. Collares-Pereira
Keyword(s):  
Performance Prediction ◽  
Test Method ◽  
Input Output ◽  
Solar Systems ◽  
Long Term Performance ◽  
Term Performance

scholarly journals Long Term Household Electricity Demand Forecasting Based on RNN-GBRT Model and a Novel Energy Theft Detection Method

2021 ◽  
Vol 11 (18) ◽  
pp. 8612
Author(s):  
Santanu Kumar Dash ◽  
Michele Roccotelli ◽  
Rasmi Ranjan Khansama ◽  
Maria Pia Fanti ◽  
Agostino Marcello Mangini
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Sudden Change ◽  
Electricity Demand ◽  
Gradient Boosting ◽  
Forecasting Model ◽  
Demand Forecast ◽  
Daily Consumption ◽  
Energy Provider

The long-term electricity demand forecast of the consumer utilization is essential for the energy provider to analyze the future demand and for the accurate management of demand response. Forecasting the consumer electricity demand with efficient and accurate strategies will help the energy provider to optimally plan generation points, such as solar and wind, and produce energy accordingly to reduce the rate of depletion. Various demand forecasting models have been developed and implemented in the literature. However, an efficient and accurate forecasting model is required to study the daily consumption of the consumers from their historical data and forecast the necessary energy demand from the consumer’s side. The proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) forecasting technique allows one to reduce the demand for electricity by studying the daily usage pattern of consumers, which would significantly help to cope with the accurate evaluation. The efficiency of the proposed forecasting model is compared with various conventional models. In addition, by the utilization of power consumption data, power theft detection in the distribution line is monitored to avoid financial losses by the utility provider. This paper also deals with the consumer’s energy analysis, useful in tracking the data consistency to detect any kind of abnormal and sudden change in the meter reading, thereby distinguishing the tampering of meters and power theft. Indeed, power theft is an important issue to be addressed particularly in developing and economically lagging countries, such as India. The results obtained by the proposed methodology have been analyzed and discussed to validate their efficacy.

scholarly journals Linkages Perekonomian Bali

2021 ◽  
Vol 2021 (1) ◽  
pp. 223-234
Author(s):  
Rizky Zulkarnain ◽  
Nasiyatul Ulfah
Keyword(s):  
Input Output ◽  
Model Input

Berbagai studi telah dilakukan untuk menganalisis dampak pengganda dari perekonomian Bali. Namun, studi-studi tersebut umumnya berfokus pada keterkaitan antar sektor menggunakan model Input-Output (IO). Padahal, perekonomian antar wilayah dapat saling bergantung melalui berbagai macam eksternalitas dan jaringan rantai suplai. Studi ini menganalisis perekonomian Bali tidak hanya berdasarkan hubungan antar sektor, namun juga mempertimbangkan hubungan ekonomi Bali dengan provinsi lainnya. Model yang digunakan adalah Inter Regional Input Output (IRIO). Tabel IRIO berukuran 17 industri x 34 provinsi diperoleh dari Badan Pusat Statistik. Hasil analisis menunjukkan bahwa terdapat beberapa industri unggulan di Provinsi Bali, yaitu Listrik dan Gas, Transportasi dan Pergudangan, Informasi dan Komunikasi, dan Jasa Perusahaan. Industri Listrik dan Gas memiliki keterkaitan antar sektor dan dampak output yang paling besar diantara industri-industri di Provinsi Bali. Selanjutnya, analisis antar wilayah menunjukkan bahwa shock permintaan akhir di Provinsi Bali berdampak besar terhadap perekonomian provinsi-provinsi di Pulau Jawa, khususnya Jawa Timur. Di sisi lain, perekonomian Bali sangat dipengaruhi oleh shock permintaan akhir di Provinsi Nusa Tenggara Barat.

scholarly journals Forecasting Australian port throughput: Lessons and Pitfalls in the era of Big Data

2019 ◽  
Author(s):  
soumya banerjee
Keyword(s):  
Big Data ◽  
Forecasting Model ◽  
Short Term ◽  
Port Development ◽  
Forecasting Accuracy ◽  
Port Throughput ◽  
Infrastructure Investments ◽  
The Cost ◽  
Modelling And Forecasting

Modelling and forecasting port throughput enables stakeholders to make efficient decisions ranging from management of port development, to infrastructure investments, operational restructuring and tariffs policy. Accurate forecasting of port throughput is also critical for long-term resource allocation and short-term strategic planning. In turn, efficient decision-making enhances the competitiveness of a port. However, in the era of big data we are faced with the enviable dilemma of having too much information. We pose the question: is more information always better for forecasting? We suggest that more information comes at the cost of more parameters of the forecasting model that need to be estimated. We comparemultiple forecasting models of varying degrees of complexity and quantify the effect of the amount of data on model forecasting accuracy. Our methodology serves as a guideline for practitioners in this field. We also enjoin caution that even in the era of big data more information may not always be better. It would be advisable for analysts to weigh the costs of adding more data: the ultimate decision would depend on the problem, amount of data and the kind of models being used.

scholarly journals Developing a Long-term Forecasting Model for National Park Demand

2018 ◽  
Vol 22 (3) ◽  
pp. 57-63
Author(s):  
SangYoel Han ◽  
권헌교
Keyword(s):  
National Park ◽  
Forecasting Model ◽  
Long Term Forecasting
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