An artificial neural network based micro-hydropower generation scheduling: considering demand profile and environmental impact

2006 ◽  
Vol 8 (2) ◽  
pp. 123-130 ◽  
Author(s):  
Noel Estoperez ◽  
Ken Nagasaka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Environmental Impact ◽  
Hydropower Generation ◽  
Generation Scheduling ◽  
Artificial Neural ◽  
Demand Profile

scholarly journals Artificial Neural Network Based Economic Generation Scheduling in Nigeria Power Network

2014 ◽  
Vol 3 (4) ◽  
pp. 206
Author(s):  
Omorogiuwa Eseosa ◽  
Onohaebi S.O
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Operating Cost ◽  
Inequality Constraints ◽  
Minimum Cost ◽  
Power Network ◽  
Load Demand ◽  
Generation Scheduling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>Economic generation scheduling determines the most efficient and economic means of dispatch of generated energy to meet the continuously varying load demand at the most appropriate minimum cost, while meeting all the units equality and inequality constraints in  power network. This is currently not applicable in Nigeria power network. The network under study consists of seventeen (17) generating stations (Existing Network, National Integrated Power Projects and the Independent Power Producers). This work investigates economic generation and scheduling in Nigeria 330KV integrated power network at minimum operating cost using the classical kirmayer’s method and Artificial Neural Network (ANN) for its optimization in Matlab environment. ANN is trained to adopt its pattern at different load demands and acquires the ability to give load demand as soon as the set target and goal tends to equality. Cost function for each generating unit as well as a model for economic generation scheduling was developed.</p>

Study on Correlation between Urban Development and Air Pollution Based on Artificial Neural Network

2013 ◽  
Vol 361-363 ◽  
pp. 860-863
Author(s):  
Jian Ping Li ◽  
Qiao Song ◽  
Hai Ying Yang ◽  
Han Ming Duan
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Artificial Neural Network ◽  
Environmental Impact ◽  
Urban Development ◽  
Impact Factors ◽  
Pollution Indicators ◽  
Pollution Indicator ◽  
Artificial Neural ◽  
The Impact

The paper presents a method of researching the impact of urban development on air quality on the basis of artificial neural network (ANN). Statistical data in a monitoring period constitute a sample which contains monitoring values of environmental impact factors and air pollution indicators. Several samples are employed to train the ANN, and the mapping relationship between environmental impact factors and air pollution indicators is established through the trained ANN. The impact degree of each environmental impact factor on each air pollution indicator can be obtained by using the connection weights of the trained ANN. The case study illustrates the feasibility of the method mentioned in the paper which explores a new idea to the study of environmental impact of urban development.

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