In this work, we summarized the characteristics and influencing factors of load forecasting based on its application status. The common methods of the short-term load forecasting were analyzed to derive their advantages and disadvantages. According to the historical load and meteorological data in a certain region of Taizhou, Zhejiang Province, a least squares support vector machine model was used to discuss the influencing factors of forecasting. The regularity of the load change was concluded to correct the "abnormal data" in the historical load data, thus normalizing the relevant factors in load forecasting. The two parameters are as follows Gauss kernel function and Eigen parameter C in LSSVM had a significant impact on the model, which was still solved by empirical methods. Therefore, the particle swarm optimization was used to optimize the model parameters. Taking the error of test set as the basis of judgment, the optimization of model parameters was achieved to improve forecast accuracy. The practical examples showed that the method in the work had good convergence, forecast accuracy, and training speed.
In recent years, there has been a rapid proliferation of research concerning Wireless Sensor Networks (WSNs), due to the wide range of potential applications that they can be used for. Lifetime is one of the most important considerations for WSNs due to their inherent energy constraints, and various protocols have been proposed to overcome these difficulties. This study proposes a novel distributed reclustering routing protocol: Predictive and Adaptive Routing Protocol using Energy Welfare (PARPEW). PARPEW incorporates the concept of energy welfare (EW) to achieve both energy efficiency and energy balance simultaneously. PARPEW is equipped with a cluster head (CH) shift mechanism that utilizes predictive energy after transmission for the computation of EW. The average case time complexity of the shift mechanism belongs to O(|C| 2 ), where |C| is the average number of sensors in a cluster in the WSN. Experimental results demonstrate that the new protocol is capable of significantly prolonging the lifetime of WSNs under various scenarios.
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