Pattern Recognition and Predictive Modelling in Smart Grids

Abstract

Smart grids are a modification of traditional electric power grid to achieve a bidirectional, automatic, intelligent and adaptive power system. In smart grids the electricity distribution and power system management is improved by leveraging advanced two-way communications and integrating computing capabilities to achieve better reliability, stability, efficiency, and security of the power system. The smart grid introduces the two-way flow of data between electricity suppliers and customers to transfer real-time information and facilitate the near real-time balance of supply-demand management. In contrast to many other industries who have the capability to store and reserve their products, the electric power industry does not have such a capability to store a massive amount of electricity using today’s technologies. Therefore, due to the storage limitations of electricity, one of the crucial tasks of power system operation is to keep a balance between supply and demand at every moment. As a result, forecasting is an essential and important function in the electricity power grid.

Recent advances in the energy industry, including smart grid and smart meters, provide new capabilities to electrical utilities for forecasting electricity demand, modelling customers’ usage profiles, optimizing unit commitment and preventing outages. These advances also introduce new challenges to the power grid, such as managing and analysing of large volumes of complex, high dimensional data in an efficient manner. So, utilities need to apply advanced data management and analytical models to extract actionable insights from this information. By leveraging better predictive and analytical models and the high volume of data, utility companies are able to produce a wide range of forecasts including: 1. Forecasting the amount of excess energy generation, the appropriate time to sell it, and the feasibility of transmitting it into the grid 2. Forecasting when and where contingencies are most likely to happen 3. Identifying the customers that are most likely to transfer energy back to the grid 4. Identifying the customers that are most likely to respond to demand reduction incentives and energy conservation programs 5. Considering the generation of distributed energy resources in the decision-making process to manage the commitment of conventional plants 6. Considering the integration of renewable energy resources into the power grid, which are inherently intermittent, weather dependent and unpredictable, to run a clean and reliable power system. To achieve these potential benefits, grid operators require accurate and efficient methods to mine patterns in customers and grid data, which can be integrated into their decision-making frameworks. In this thesis, we develop new predictive machine learning algorithms to help address the new challenges in the smart grid era.

In the first part of this thesis, we focus on understanding customers’ energy consumption behaviour (demand analytics). Previously, information about customers’ energy consumption could be obtained only with coarse granularity (e.g., monthly or bimonthly), Nowadays, using advanced metering infrastructure (or smart meters), utility companies are able to retrieve it in near real-time. By leveraging smart meter data, we propose a hierarchical demand forecasting approach. We improve the aggregated level of electricity load forecasts by first segmenting the households into several clusters, forecasting the energy consumption of each cluster, and then aggregating those forecasts. The improvements provided by this strategy depend not only on the number of clusters, but also on the size of the clusters and selecting an appropriate clustering method. We also leverage deep learning techniques to improve forecast accuracy.

Dealing with the high volume of time-series data (smart meter data) has motivated us to develop a new clustering algorithm for time-series data that is computationally efficient and accurate. In the second part of this thesis, we introduce our two new proposed clustering algorithms namely, “Fuzzy C-Shape plus (FCS+)” and “Fuzzy C-Shape plus plus (FCS++)” and we show that the two new algorithms outperform state-of-the-art shape-based clustering algorithms in terms of accuracy and efficiency.

Improving accuracy is a primary goal in any forecasting task, which is especially challenging in multi-step prediction scenarios. In the third part of this thesis we propose a robust and accurate ensemble-based load forecasting framework to address some of the challenges associated with load forecasting, including unbalanced training load data, the non-stationary nature of the load data, and feature selection for predictive modelling. The performance of the proposed method is validated with real-life data from the power system in the Australian National Electricity Market, as well as through on-site implementation by the system operator.

In practice, an understanding of the uncertainty in the forecasts that an operational power grid uses is crucial in order to operate the system in a secure manner in real-time and into the future. In the fourth part of this thesis, we propose a dynamic stochastic decision support tool, based on Dynamic Bayesian Belief Networks, to quantify the level of uncertainty in order to improve situational awareness and understand the risks to power system operators. The performance of the proposed method is validated on real-life data from the Australian power system, and through actual on-site implementation by the Australian system operator, the Australian Energy Market Operator.