Photovoltaic (PV) power is volatile in nature and raises the level of uncertainty in power systems. PV power forecasting is an important measure to solve this problem. It helps to improve the reliability and reduces the generation cost. Advances in computer technology and sensors make the numeric modeling methods a hotspot in the field of PV power forecasting. However, data modeling methods strongly rely on the accuracy of measurement data. Unavoidable outliers in the measured meteorological data have an adverse effect on the model due to their heteroscedasticity. Although many studies can be found focusing on outlier detection, only a few have incorporated outlier detection with regression models. In this study, an innovative method employing the weighted Gaussian process regression approach is proposed, such that data samples with higher outlier potential have a low weight. A density-based local outlier detection approach is introduced to compensate the deterioration of Euclidean distance for high-dimensional data. A novel concept of the degree of nonlinear correlation is incorporated to compute the contribution of every individual data attribute. Effectiveness of the proposed method is demonstrated by performing an experimental analysis and making comparisons with other typical data-based approaches, and the results exhibit higher estimation accuracy.

Trust and distrust are common in the opinion interactions among agents in signed social networks. To explain the gradual evolution of the trust/distrust level between agents due to opinion difference, this paper proposes a new opinion dynamic model with asynchronous evolution of edge weights. In the model, the trust/distrust level between agents is portrayed as a nonlinear weight function of their opinion difference, and each agent only interacts with the neighbors at the times determined by its own will. In the presence of a stubborn leader with a firm stand, a comprehensive theoretical analysis for opinion consensus and opinion polarization is performed by using the related properties of sub-stochastic and super-stochastic matrices. Moreover, the opinion dynamics over an arbitrary underlying network with multiple non-stubborn leaders is further explored. Finally, some numerical simulations are provided to illustrate the theoretical results.

Lithium batteries have the characteristics of high energy density and charge-discharge rate, but exhibit high chemical activity. State-of-charge (SOC) estimation is critical to the lithium battery electric vehicle (EV) operation safety. In this paper, a novel SOC estimation method is proposed based on Gaussian process regression. A mixture Gaussian process is used in this model to strengthen the reliability of data description and to increase the estimation accuracy. Optimal number of Gaussian processes is obtained by a revolutionary expectation maximum method. A nonlinear correlation feature selection method is introduced to improve the model efficiency. The effectiveness of the proposed method is verified by an EV field test. Compared with other data-based approaches, this method exhibits higher estimation accuracy and computational efficiency.

Machine learning methods are expected to play a significant role in battery state of charge (SOH) estimation, leveraging their strengths in self-learning and nonlinear fitting. One of the key challenges in SOH estimation is the concept drift issue, which refers to changes in the data distribution between the training and test datasets. General machine learning methods assume that the training data shares similar characteristics with the test data. However, in SOH estimation tasks, differences in the environment and the characteristics of the battery itself can cause concept drift, which then impacts the model's effectiveness. As a result, many data-driven models that perform well in laboratory conditions struggle to be applied to other target batteries. This is a common and significant battery diagnosis technology issue, yet it remains unresolved. This paper proposes a multi-domain transfer Gaussian process regression (MTR-GPR) SOH estimation approach to address this issue. In this model, training data do not directly participate in the model's learning process. Instead, the MTR-GPR model extracts information from different datasets based on the distribution similarity. This method can fully use multi-source battery ageing data while reducing the negative impact of distribution differences. Experimental results prove that MTR-GPR can make reliable SOH estimates with only 20% of target battery data. On the other hand, this method can provide the posterior probability distribution of the prediction results