The foundation of energy saving is knowing the real status of building energy consumption. For various kinds and a great number of building energy consumption data, the fuzzy theory is applied for sampling. It would make data representational. Firstly, a fuzzy clustering method is used to classify the data set and then the samples are extracted from the subclass. A modified clustering algorithm based on entropy weight method is proposed. It can determine the number of the classification of data set. The simulation results indicate that the new method can directly determine the optimal sample size. This method is suitably applied for dynamic energy consumption data and is more accurate compared with the statistical method.
A new residual life prediction method for complex systems based on Wiener process and evidential reasoning is proposed to predict the residual life of complex systems effectively. Moreover, the better maintenance strategies and decision supports are provided. For the residual life prediction of complex systems, the maximum likelihood method is adopted to estimate the drift coefficient, and the Bayesian method is adopted to update the parameters of Wiener process. The process of parameters estimation and the probability density function (PDF) of the residual life are deduced. To improve the accuracy of the residual life prediction results, the evidential reasoning (ER) is used to integrate the prediction results of Wiener process. Finally, a case study of gyroscope is examined to illustrate the feasibility and effectiveness of the proposed method, compared with fuzzy theory, which provides an important reference for the optimization of the reliability of complex systems and improvement.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.