Energy big data: A survey <s> II. BACKGROUND A. SMART GRID <s> Demand response (DR), distributed generation (DG), and distributed energy storage (DES) are important ingredients of the emerging smart grid paradigm. For ease of reference we refer to these resources collectively as distributed energy resources (DER). Although much of the DER emerging under smart grid are targeted at the distribution level, DER, and more specifically DR resources, are considered important elements for reliable and economic operation of the transmission system and the wholesale markets. In fact, viewed from transmission and wholesale operations, sometimes the term ?virtual power plant? is used to refer to these resources. In the context of energy and ancillary service markets facilitated by the independent system operators (ISOs)/regional transmission organizations (RTOs), the market products DER/DR can offer may include energy, ancillary services, and/or capacity, depending on the ISO/RTO market design and applicable operational standards. In this paper we first explore the main industry drivers of smart grid and the different facets of DER under the smart grid paradigm. We then concentrate on DR and summarize the existing and evolving programs at different ISOs/RTOs and the product markets they can participate in. We conclude by addressing some of the challenges and potential solutions for implementation of DR under smart grid and market paradigms. <s> BIB001 </s> Energy big data: A survey <s> II. BACKGROUND A. SMART GRID <s> Minimax probability machine (MPM) is an interesting discriminative classifier based on generative prior knowledge. It can directly estimate the probabilistic accuracy bound by minimizing the maximum probability of misclassification. The structural information of data is an effective way to represent prior knowledge, and has been found to be vital for designing classifiers in real-world problems. However, MPM only considers the prior probability distribution of each class with a given mean and covariance matrix, which does not efficiently exploit the structural information of data. In this paper, we use two finite mixture models to capture the structural information of the data from binary classification. For each subdistribution in a finite mixture model, only its mean and covariance matrix are assumed to be known. Based on the finite mixture models, we propose a structural MPM (SMPM). SMPM can be solved effectively by a sequence of the second-order cone programming problems. Moreover, we extend a linear model of SMPM to a nonlinear model by exploiting kernelization techniques. We also show that the SMPM can be interpreted as a large margin classifier and can be transformed to support vector machine and maxi–min margin machine under certain special conditions. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of SMPM. <s> BIB002
Smart grid is a form of electric power network. It is a combination of all the power utilities resources, both of renewable and non-renewable. As a significant innovation, smart grid provides automatic monitoring, protecting and optimizing for the operation of the interconnected systems. Not only covering the traditional central generator in the transmission network and emerging renewal distributed generator in the distribution system, it also serves industrial consumers and home users in aspects of thermostats, electric vehicles and intelligent appliances BIB002 . Characterized by the bidirectional connection of electricity and information sets, the smart grid has formed an automated, widely distributed delivery power network. Taking advantages from modern communications, it can not only deliver real-time data and information but also implement the instantaneous management balance of demand and supply BIB001 . Operations on energy storage, demand response and communication between terminal users and power companies will make a real large scale data-communicated grid system. This adds complexity in the conventional grid and promote offering sustainable progresses to utilities and customers  . In order to match its complexity, many technologies are exploited in the smart grid domain. Examples include wireless networks in telecommunications, sensor networks in manufacturing, and big data in computer science. Especially, big data is attracting more and more attention in the power grid domain because it can help extract valuable information from data sources of wide diversity and high volume.