Next Day Load Demand Forecasting of Future in Electrical Power Generation on Distribution Networks using Adaptive Neuro-Fuzzy Inference

2006 ◽  
Author(s):  
Chamni Jaipradidtham
Keyword(s):  
Power Generation ◽  
Electrical Power Generation ◽  
Fuzzy Inference ◽  
Electrical Power ◽  
Demand Forecasting ◽  
Distribution Networks ◽  
Load Demand ◽  
Neuro Fuzzy

scholarly journals Optimization of Biogas Electrical Power Generation using Neuro-Fuzzy Controller

2020 ◽  
Vol 7 (6) ◽  
pp. 21-29
Author(s):  
Araoye Timothy Oluwaseun ◽  
Alor Michael Onyeamaechi ◽  
Okika Stephen Sunday
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Power System ◽  
Electrical Power Generation ◽  
Power Output ◽  
Fuzzy Controller ◽  
Electrical Power ◽  
Fuzzy Model ◽  
Neuro Fuzzy ◽  
Electrical Power Output

Biogas electrical power generation is a renewable energy which originated from biological materials. The technology design and model power system that predict and control the generation of biogas Electrical production. This research paper develops a Neuro-fuzzy controller model for generation of Biogas power production. A Neuro-fuzzy controller is design to the Biogas power system in order to improve the power quality delivery to the load. The set of 27 rules are written for proper training of biogas electrical data in the neural network. The training is used to control signal of the Biogas Power output of the system. The  output  of  Neural Network  unit  is  given  as  input  to  the de-fuzzification  unit and the linguistic variables are converted back into the crisp form. Therefore the algorithm was designed to decide power supply to the load as to improve the performance of the biogas system using MATLAB/SIMULINK and Neuro-fuzzy model was developed for easy input of the data. The result shows that biogas electrical power output increased by 4.39kw, which is 54.8% increase when Neuro-fuzzy controller is incorporated. The improvement in the system is due to the training of input parameters of the biogas generated. The result obtained shows that there is Real Power improvement in Biogas system when Neuro-fuzzy is incorporated in the system model

scholarly journals Short-Term Electric Power Forecasting Using Dual-Stage Hierarchical Wavelet- Particle Swarm Optimization- Adaptive Neuro-Fuzzy Inference System PSO-ANFIS Approach Based On Climate Change

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2822 ◽  
Author(s):  
Samuel Atuahene ◽  
Yukun Bao ◽  
Yao Ziggah ◽  
Patricia Gyan ◽  
Feng Li
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Fuzzy Inference ◽  
Electrical Power ◽  
Swarm Optimization ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Dual Stage ◽  
Power Forecasting

Analyzing electrical power generation for a wind turbine has associated inaccuracies due to fluctuations in environmental factors, mechanical alterations of wind turbines, and natural disaster. Thus, development of a highly reliable prediction model based on climatic conditions is crucial in forecasting electrical power for proper management of energy demand and supply. This is essential because early forecasting systems will enable an energy supplier to schedule and manage resources efficiently. In this research, we have put forward a novel electrical power prediction model using wavelet and particle swarm optimization based dual-stage adaptive neuro-fuzzy inference system (dual-stage Wavelet-PSO-ANFIS) for precise estimation of electrical power generation based on climatic factors. The first stage is used to project wind speed based on meteorological data available, while the second stage took the output wind speed prediction to predict electrical power based on actual supervisory control and data acquisition (SCADA). Furthermore, influence of data dependence on the forecasting accuracy for both stages is analyzed using a subset of data as input to predict the wind power which was also compared with other existing electrical power forecasting techniques. This paper defines the basic framework and the performance evaluation of a dual-stage Wavelet-PSO-ANFIS based electrical power forecasting system using a practical implementation.

Experiments on High-Temperature Inert Gas Plasma MHD Electrical Power Generation with Hall and Diagonal Connections

2015 ◽  
Vol 193 (3) ◽  
pp. 17-23 ◽  
Author(s):  
Fumihiko Komatsu ◽  
Manabu Tanaka ◽  
Tomoyuki Murakami ◽  
Yoshihiro Okuno
Keyword(s):  
Power Generation ◽  
High Temperature ◽  
Electrical Power Generation ◽  
Electrical Power ◽  
Inert Gas ◽  
Gas Plasma

Analysis of Electrical Power Generation Costs

1977 ◽  
Vol 33 (2) ◽  
pp. 212-222 ◽  
Author(s):  
R. W. Hardie ◽  
J. H. Chamberlin
Keyword(s):  
Power Generation ◽  
Electrical Power Generation ◽  
Electrical Power ◽  
Generation Costs

Sir Charles Parsons and Electrical Power Generation—a Turbine Designer's Perspective

1994 ◽  
Vol 208 (3) ◽  
pp. 159-176 ◽  
Author(s):  
J R Bolter
Keyword(s):  
Power Generation ◽  
Steam Turbine ◽  
Gas Turbine ◽  
Electrical Power Generation ◽  
Analytical Methods ◽  
State Of The Art ◽  
Electrical Power ◽  
Turbine Generator ◽  
Current State ◽  
The World

Sir Charles Parsons died some three years after the author was born. In this paper the author looks back at the pioneering work of Parsons in the field of power generation. It shows how he was able to increase output of the steam turbine generator from 7.5 kW in 1884 to 50000 kW in 1930 while increasing efficiency from 1.6 to 36 per cent, and relates these achievements to the current state of the art. Blading design, rotor construction and other aspects of turbine engineering are considered. The conclusion is that Parsons and his associates charted the course which manufacturers and utilities throughout the world have continued to follow, although increasingly sophisticated design and analytical methods have succeeded the intuitive approach of Parsons. His constant search for improved efficiency was and is highly relevant to today's concern for the environment. Finally, although it did not become a practical proposition in his lifetime, the paper reviews Parsons' vision of, and continuing interest in, the gas turbine, first mentioned in his 1884 patents.

scholarly journals Recent advances in open cycle MHD electrical power generation

Sadhana ◽  
1984 ◽  
Vol 7 (1) ◽  
pp. 1-72
Author(s):  
V K Rohatgi ◽  
N Venkatramani
Keyword(s):  
Power Generation ◽  
Electrical Power Generation ◽  
Electrical Power ◽  
Recent Advances ◽  
Open Cycle
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