Designing an optimized model to forecast short-term electricity demand based on ARIMA and wavelet decomposition neural network: composition of linear and non-linear model (a case study in Iran)

2013 ◽  
Author(s):  
M. Zolfaghari ◽  
F. Behdad ◽  
F. Besharatnia
Keyword(s):  
Neural Network ◽  
Linear Model ◽  
Wavelet Decomposition ◽  
Electricity Demand ◽  
Short Term ◽  
Non Linear ◽  
Network Composition ◽  
Optimized Model

A NON-LINEAR MODEL FOR OPTIMAL ALLOCATION OF IRRIGATION WATER AND LAND UNDER ADEQUATE AND LIMITED WATER SUPPLIES: A CASE STUDY IN SOUTHERN ITALY

2013 ◽  
Vol 62 (2) ◽  
pp. 145-155 ◽  
Author(s):  
Pietro Rubino ◽  
Anna Maria Stellacci ◽  
Roberta M. Rana ◽  
Maurizia Catalano ◽  
Angelo Caliandro
Keyword(s):  
Linear Model ◽  
Irrigation Water ◽  
Optimal Allocation ◽  
Southern Italy ◽  
Water Supplies ◽  
Non Linear

Non-Linear Model in Architectural Design for Sustainable Social Housing: Case Study Ovča Housing Project Belgrade

2015 ◽  
Vol 40 (4) ◽  
pp. 30-36
Author(s):  
Dušan Stojanović ◽  
Pavle Stamenović
Keyword(s):  
Linear Model ◽  
Design Process ◽  
Social Housing ◽  
Architectural Design ◽  
Common Ground ◽  
Sustainable Living ◽  
Housing Design ◽  
Non Linear ◽  
Spatial Needs

The aim of this paper is to reconsider the conventional approaches in architectural design for social housing that lead to low adaptability of architecture regarding spatial needs of its inhabitants. This research explores the potential of nonlinear model in architectural design of sustainable social housing. Sustainability is commonly interpreted through categories of socio-economic availability, notwithstanding the fact that demands of contemporary living greatly exceed the scope of this definition. One of the methods to integrate sustainability into social housing design is to incorporate specific users’ needs into the design process itself. The aim is to specify the common ground for negotiation between all actors in the process. Such a platform could enable multiple options allowing flexibility and a higher level of quality, as well as the comfort of sustainable living. This design approach is developed in the case study project for Ovča social housing community in Belgrade. This project is conceived as an infrastructural system that precedes the building as a finite architecture, therefore anticipating inhabitants’ involvement in the design process. The non-linear model of architectural design is enabled trough a drawing as a tool of communication. Since it is carried out according to previously defined values, this iterative procedure establishes a specific set of outputs that can later be evaluated and modified in accordance to users’ spatial needs. Therefore, the drawing becomes a tool that allows a variety of designing processes while the most important role still belongs to the architect and the user. Such iterative design process creates preconditions that enable the inhabitants to appropriate the space of living, which legitimizes the aim to transfer the design process from conventional towards the non-linear model of architectural design.

scholarly journals ARTIFICIAL NEURAL NETWORK BASE SHORT-TERM ELECTRICITY LOAD FORECASTING: A CASE STUDY OF A 132/33KV TRANSMISSION SUB-STATION

2020 ◽  
Vol 10 (2) ◽  
pp. 200-205
Author(s):  
Isaac Adekunle Samuel ◽  
Segun Ekundayo ◽  
Ayokunle Awelewa ◽  
Tobiloba Emmanuel Somefun ◽  
Adeyinka Adewale
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Electricity Load ◽  
Artificial Neural ◽  
Electricity Load Forecasting

Applied Research Of BP Neural Network In Earthquake Prediction

2012 ◽  
Vol 204-208 ◽  
pp. 2449-2454 ◽  
Author(s):  
Wu Sheng Hu ◽  
Hong Lin Nie ◽  
Hao Wang
Keyword(s):  
Neural Network ◽  
Earthquake Prediction ◽  
Bp Neural Network ◽  
Integrated Model ◽  
Earthquake Magnitude ◽  
Linear Regression Method ◽  
Network Technology ◽  
Short Term ◽  
Non Linear ◽  
Neural Network Technology

Nowadays, earthquake prediction is still a worldwide scientific problem, especially the prediction for short-term and imminent earthquake has no substantial breakthroughs. BP neural network technology has a strong non-linear mapping function which could better reflect the strong non-linear relationship between earthquake precursors and the time and the magnitude of a potential earthquake. In this paper, we selected the region of Beijing as the research area and 3 months as the prediction period. Based on BP neural network and integrated with the conventional linear regression method, a regional short-term integrated model was established, which gives the quantitative prediction for the earthquake magnitude. The results show that the earthquake magnitude prediction RMSE (root mean square error) of the integrated model reaches ± 0.28 Ms. Compared with conventional methods, the integrated model improves significantly. The new model has a good prospect to use BP neural network technology for earthquake prediction.

scholarly journals Improving applicability of neuro-genetic algorithm to predict short-term water level: a case study

2013 ◽  
Vol 16 (1) ◽  
pp. 218-230 ◽  
Author(s):  
Gooyong Lee ◽  
Sangeun Lee ◽  
Heekyung Park
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Water Level ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
River Management ◽  
Water Levels ◽  
Short Term

This paper proposes a practical approach of a neuro-genetic algorithm to enhance its capability of predicting water levels of rivers. Its practicality has three attributes: (1) to easily develop a model with a neuro-genetic algorithm; (2) to verify the model at various predicting points with different conditions; and (3) to provide information for making urgent decisions on the operation of river infrastructure. The authors build an artificial neural network model coupled with the genetic algorithm (often called a hybrid neuro-genetic algorithm), and then apply the model to predict water levels at 15 points of four major rivers in Korea. This case study demonstrates that the approach can be highly compatible with the real river situations, such as hydrological disturbances and water infrastructure under emergencies. Therefore, proper adoption of this approach into a river management system certainly improves the adaptive capacity of the system.

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