A Comprehensive Learning-Based Model for Power Load Forecasting in Smart Grid

In the big data era, learning-based techniques have attracted more and more attention in many industry areas such as smart grid, intelligent transportation. The power load forecasting is one of the most critical issues in data analysis of smart grid. However, learning-based methods have not been widely used due to the poor data quality and computational capacity. In this paper, we propose a comprehensive learning-based model to forecast heavy and over load (HOL) accidents according to the data from various information systems. At first, we present a combined random under- and over-sampling technique for imbalanced electric data, and choose an optimal sampling rate through several experiments. Then, we reduce the attributes that have significant impact on the power load by using learning-based methods. Finally, we provide an algorithm based on the random forest method to prevent the over-fitting problem. We evaluate the proposed model and algorithms with the real-world data provided by China Grid. The experimental results show that our model works efficiently and achieves low error rates

Deep learning driven data analytics for smart grids

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonAs advanced metering infrastructure (AMI) and wide area monitoring systems (WAMSs) are being deployed rapidly and widely, the conventional power grid is transitioning towards the smart grid at an increasing speed. A number of smart metering devices and real-time monitoring systems are capable to generate a huge volume of data on a daily basis. However, a variety of generated data can be made full use of to advance the development of the smart grid through big data analytics, especially, deep learning. Thus, the thesis is focused on data analysis for smart grids from three different aspects. Firstly, a real-time data driven event detection method is presented, which is quite robust when dealing with corrupted and significantly noisy data of phase measurement units (PMUs). To be specific, the presented event detection method is based on a novel combination of random matrix theory (RMT) and Kalman filtering. Furthermore, a dynamic Kalman filtering technique is proposed through the adjustment of the measurement noise covariance matrix as the data conditioner of the presented method in order to condition PMU data. The experimental results show that the presented method is indeed quite robust in such practical situations that include significant levels of noisy or missing PMU data. Secondly, a short-term residential load forecasting method is proposed on the basis of deep learning and k-means clustering, which is capable to extract similarity of residential load effectively and perform prediction accurately at the individual residential level. Specifically, it makes full use of k-means clustering to extract similarity among residential load and deep learning to extract complex patterns of residential load. In addition, in order to improve the forecasting accuracy, a comprehensive feature expression strategy is utilised to describe load characteristics of each time step in detail. The experimental results suggest that the proposed method can achieve a high forecasting accuracy in terms of both root mean square error (RMSE) and mean absolute error (MAE). Thirdly, an online individual residential load forecasting method is developed based on a combination of deep learning and dynamic mirror descent (DMD), which is able to predict residential load in real time and adjust the prediction error over time in order to improve the prediction performance. More specifically, it firstly employs a long short term memory (LSTM) network to build a prediction model offline, and then applies it online with DMD correcting the prediction error. In order to increase the prediction accuracy, a comprehensive feature expression strategy is used to describe load characteristics at each time step in detail. The experimental results indicate that the developed method can obtain a high prediction accuracy in terms of both RMSE and MAE. To sum up, the proposed real-time event detection method contributes to the monitoring and operation of smart grids, while the proposed residential load forecasting methods contribute to the demand side response in smart grids.TDX-ASSIS

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions