Power Aware Routing in Mobile Ad Hoc Networks

The Grid 2: Blueprint for a New Computing Infrastructure

해외산업간호정보 - 미국 산업안전보건연구원(National Institute for Occupational Safety and Health) 소개

(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.

Designing an Super-Peer Network

As a clean and renewable energy source, wind energy has been increasingly gaining global attention. Wind speed forecast is of great significance for wind energy domain: planning and design of wind farms, wind farm operation control, wind power prediction, power grid operation scheduling, and more. Many wind speed forecasting algorithms have been proposed to improve prediction accuracy. Few of them, however, have studied how to select input parameters carefully to achieve desired results. After introducing a Back Propagation neural network based on Particle Swam Optimization (PSO-BP), this paper details a method called IS-PSO-BP that combines PSO-BP with comprehensive parameter selection. The IS-PSO-BP is short for Input parameter Selection (IS)-PSO-BP, where IS stands for Input parameter Selection. To evaluate the forecast performance of proposed approach, this paper uses daily average wind speed data of Jiuquan and 6-hourly wind speed data of Yumen, Gansu of China from 2001 to 2006 as a case study. The experiment results clearly show that for these two particular datasets, the proposed method achieves much better forecast performance than the basic back propagation neural network and ARIMA model.

Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting

This paper proposes a novel combined electricity demand forecasting method, which based on Back Propagation (BP) neural network, Adaptive Network-based Fuzzy Inference System (ANFIS) and Difference Seasonal Autoregressive Integrated Moving Average (diff-SARIMA).The combined method eliminates drawbacks and incorporates in the merits of the individual methods. It has the capability to deal with the linearity, nonlinearity and seasonality data.Experimental case study shows that the proposed combined method performed better than the other three individual methods and had a higher accuracy. And the proposed method also performed better than the method ESPLSSVM that I proposed before. Electricity demand forecasting, as a vital tool in the electricity market, plays a critical role in power utilities, which can not only reduce production costs but also save energy resources, thus making the forecasting techniques become an indispensable part of the energy system. A novel combined forecasting method based on Back Propagation (BP) neural network, Adaptive Network-based Fuzzy Inference System (ANFIS) and Difference Seasonal Autoregressive Integrated Moving Average (diff-SARIMA) are presented in this paper. Firstly, the combined method uses all the three methods (BP, ANFIS, diff-SARIMA) to forecast respectively, and the three forecasting results were obtained. By multiplying optimal weight coefficients of the three forecasting results respectively and then adding them up, in the end the final forecasting results can be obtained. Among the three individual methods, BP and ANFIS had the ability to deal with the nonlinearity data, and diff-SARIMA had the ability to deal with the linearity and seasonality data. So the combined method eliminates drawbacks and incorporates in the merits of the individual methods. It has the capability to deal with the linearity, nonlinearity and seasonality data. In order to optimize weight coefficients, Differential Evolution (DE) optimization algorithm is brought into the combined method. To prove the superiority and accuracy, the capability of the combined method is verified by comparing it with the three individual methods. The forecasting results of the combined method proved to be better than all the three individual methods and the combined method was able to reduce errors and improve the accuracy between the actual values and forecasted values effectively. Using the half-hour electricity power data of the State of New South Wales in Australia, relevant experimental case studies showed that the proposed combined method performed better than the other three individual methods and had a higher accuracy.

Lian Li

Papers

Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting

Modelling a combined method based on ANFIS and neural network improved by DE algorithm

Mixed kernel based extreme learning machine for electric load forecasting

A hybrid application algorithm based on the support vector machine and artificial intelligence: An example of electric load forecasting

A novel combined model based on echo state network for multi-step ahead wind speed forecasting: A case study of NREL