You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Energy big data: A survey 
Chapter Title:  1) MAIN TECHNIQUES 
1. Abstract of Cited Paper (BIB001):  Abstract This paper presents an efficient method for the reconfiguration of radial distribution systems for minimization of real power loss using adapted ant colony optimization. The conventional ant colony optimization is adapted by the graph theory to always create feasible radial topologies during the whole evolutionary process. This avoids tedious mesh check and hence reduces the computational burden. The initial population is created randomly and a heuristic spark is introduced to enhance the pace of the search process. The effectiveness of the proposed method is demonstrated on balanced and unbalanced test distribution systems. The simulation results show that the proposed method is efficient and promising for reconfiguration problem of radial distribution systems. 
2. Abstract of Cited Paper (BIB002):  The introduction of remotely controlled network devices is transforming the way the power system is operated and studied. The ability to provide real and reactive power support can be achieved at the end-user level. In this paper, a framework and algorithm to coordinate this type of end-user control is presented. The algorithm is based on a layered architecture that would follow a chain of command from the top layer (transmission grid) to the bottom layer (distribution grid). At the distribution grid layer, certain local problems can be solved without the intervention of the top layers. A reactive load control optimization algorithm to improve the voltage profile in distribution grid is presented. The framework presented in this paper integrates agent-based technologies to manage the data and control actions required to operate this type of architecture. 
3. Abstract of Cited Paper (BIB003):  Power load forecasting is an essential tool for energy management systems. Accurate load forecasting supports power companies to make unit commitment decisions and schedule maintenance plans appropriately. In addition to minimizing the power generation costs, it is also important for the reliability of energy systems. This research study presents the implementation of a novel fuzzy wavelet neural network model on an hourly basis, and validates its performance on the prediction of electricity consumption of the power system of the Greek Island of Crete. In the proposed framework, a multiplication wavelet neural network has replaced the classic linear model, which usually appears in the consequent part of a neurofuzzy scheme, while subtractive clustering with the aid of the Expectation–Maximization algorithm is being utilized in the definition of fuzzy rules. The results related to the minimum and maximum load using metered data obtained from the power system of the Greek Island of Crete indicate that the proposed forecasting model provides significantly better forecasts, compared to conventional neural networks models applied on the same dataset. 
4. Abstract of Cited Paper (BIB004):  Detailed large-scale simulations require a lot of data. Residential electrical load profiles are well protected by privacy laws. Representative residential electrical load generators get around the privacy problem and allow for Monte Carlo simulations. A top-down model of the residential electrical load, based on a dataset of over 1300 load profiles, is presented in this paper. The load profiles are clustered by a Mixed Model to group similar ones. Within the group, a behavior model is constructed with a Markov model. The states of the Markov models are based on the probability distribution of the electrical power. A second Markov model is created to randomize the behavior. A load profile is created by first performing a random-walking of the Markov models to get a sequence of states. The inverse of the probability distribution of the electrical power is used to translate the resulting states into electrical power. 
5. Abstract of Cited Paper (BIB005):  Abstract This work presents a method for the selection, typification and clustering of load curves (STCL) capable of recognizing consumption patterns in the electricity sector. The algorithm comprises four steps that extract essential features from the load curve of residential users with an emphasis on their seasonal and temporal profile, among others. The method was successfully implemented and tested in the context of an energy efficiency program carried out by the Energy Company of Maranhao (Brazil). This program involved the replacement of refrigerators in low-income consumers’ homes in several towns located within the state of Maranhao (Brazil). The results were compared with a well known time series clustering method already established in the literature, Fuzzy CMeans (FCM). The results reveal the viability of the STCL method in recognizing patterns and in generating conclusions coherent with the reality of the electricity sector. The proposed method is also useful to support decision-making at management level. 
6. Abstract of Cited Paper (BIB006):  We present here a combined aggregative short-term load forecasting method for smart grids, a novel methodology that allows us to obtain a global prognosis by summing up the forecasts on the compounding individual loads. More accurately, we detail here three new approaches, namely bottom-up aggregation (with and without bias correction), top-down aggregation (with and without bias correction), and regressive aggregation. Further, we have devised an experiment to compare their results, evaluating them with two datasets of real data and showing the feasibility of aggregative forecast combinations for smart grids. 
7. Abstract of Cited Paper (BIB007):  In this paper, we tackle client load estimation in a smart grid network. For that purpose, we propose an individual model based on nonparametric estimators. The model is designed for power estimations for use in network planning. Completely data-driven, the proposed methodology can be applied to both thermosensitive and nonthermosensitive clients. Real measurements collected in French distribution systems are used to validate our methodology. The proposed approach produces more reliable estimation results than the current model (termed BAGHEERA in this paper) of the French electricity company EDF does on the same data. 
8. Abstract of Cited Paper (BIB008):  This paper proposes a new scheme for measurement, identification, and classification of various types of power quality (PQ) disturbances. The proposed method employs a fast variant of S-Transform (ST) algorithm for the extraction of relevant features, which are used to distinguish among different PQ events by a fuzzy decision tree (FDT)-based classifier. Various single as well as simultaneous power signal disturbances have been simulated to demonstrate the efficiency of the proposed technique. The simulation result implies that the proposed scheme has a higher recognition rate while classifying simultaneous PQ faults, unlike other methods. The Fast dyadic S-transform (FDST) algorithm for accurate time-frequency localization, Decision Tree algorithms for optimal feature selection, Fuzzy decision rules to complement overlapping patterns, robust performance under different noise conditions and a relatively simple classifier methodology are the strengths of the proposed scheme.

Literature Review:
Over the past few years, almost all major companies have launched their projects on big data, and brought a set of tools which are brand new to the power industry. Those companies include IBM, Oracle from IT domain, General Electric, Siemens from the power grid domain and start-ups like AutoGrid, Opower, and C3  . In addition, scientists have proposed a variety of techniques including optimization and data mining to capture, analyze, process and visualize big data that is of large volume and needed to be handled within limited time. In order to tackle communication problems and data processing problems in the smart grid area, a variety of emerging scalable and distributed architectures and frameworks have been proposed. Rogers et al. BIB001 proposed an authenticated control framework for distributed voltage supported on the smart grid. A layered architecture was presented for an optimized algorithm to follow a chain of command from the transmission grid to the distribution grid BIB002 . In addition, many optimization methods have been exploited to solve quantitative problems in smart grid branches such as power distribution, sensor topology, etc. Furthermore, Aquino-Lugo et al. BIB003 provided another analytical framework using agent-based technologies to manage data processing. Data mining extracts valuable information from data sets. It includes a series of clustering, classification and regression. A typical example of data mining under the smart grid scenario is the grouping process of customers. Those customers are grouped into different classes according to their electricity consumption types and other characteristics. Then, the clustering method is applied to generating residential load documents BIB005 and differentiating pattern changes caused by seasonal and temporal factors BIB006 . In smart grid, this data mining technology can be classified into two categories: one is Artificial Intelligence (AI) for modeling risk and uncertainty of obtained data BIB007 , and the other is estimation based on recorded data. Utilizing fuzzy wavelet neural network BIB004 and estimating with nonparametric methods BIB008 , data mining methods obtain enforcement recently. For example, the fuzzy decision tree has been used to classify disturbances of power quality BIB009 .