TY  - JOUR
N2  - In this paper, a modified backpropagation neural network is combined with a chaos-search genetic algorithm and simulated annealing algorithm for very short term electrical energy demand prediction in deregulated power industry. Multiple modifications are carried out on the conventional backpropagation algorithm such as improvements in the momentum factor and adaptive learning rate. In the hybrid scheme, the initial parameters of the modified neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. The solution set provided by the optimized genetic algorithm is further improved by using the strong local search ability of simulated annealing algorithm. The real data of New South Wales, Australian grid, is used in the experimentation for 1-h-ahead forecast with an emphasis on data analysis and preprocessing framework. The correlation analysis is used for the identification and selection of the most influential input variables. The simulation results reveal that the proposed combination technique effectively enhanced the prediction accuracy as compared to the available conventional methods. The prediction of 1-h-ahead load demand is critically important for decision-making response of the modern smart grid system. The acceptable precision of the proposed model concludes that it can be applied in the smart grid to enhance its demand responsiveness and other intelligent features. © 2016, The Natural Computing Applications Forum.
ID  - scholars8116
KW  - Algorithms; Backpropagation; Backpropagation algorithms; Decision making; Electric power transmission networks; Energy management; Forecasting; Genetic algorithms; Mapping; Neural networks; Optimization; Simulated annealing
KW  -  Chaotic mapping; Demand response; Electrical energy demand; Real coded genetic algorithm; Smart grid
KW  -  Smart power grids
Y1  - 2017///
A1  - Islam, B.
A1  - Baharudin, Z.
A1  - Nallagownden, P.
JF  - Neural Computing and Applications
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976328508&doi=10.1007%2fs00521-016-2408-3&partnerID=40&md5=b28ddf40515335c600666b7ec9872b47
VL  - 28
AV  - none
N1  - cited By 33
SP  - 877
TI  - Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid
PB  - Springer London
SN  - 09410643
EP  - 891
ER  -