Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting

The smart metering infrastructure has changed how electricity is measured in both residential and industrial application. The large amount of data collected by smart meter per day provides a huge potential for analytics to support the operation of a smart grid, an example of which is energy demand forecasting. Short term energy forecasting can be used by utilities to assess if any forecasted peak energy demand would have an adverse effect on the power system transmission and distribution infrastructure. It can also help in load scheduling and demand side management. Many techniques have been proposed to forecast time series including Support Vector Machine, Artificial Neural Network and Deep Learning. In this work we use Long Short Term Memory architecture to forecast 3-day ahead energy demand across each month in the year. The results show that 3-day ahead demand can be accurately forecasted with a Mean Absolute Percentage Error of 3.15%. In addition to that, the paper proposes way to quantify the time as a feature to be used in the training phase which is shown to affect the network performance

Multi-objective decision analytics for short-notice bushfire evacuation: An Australian case study

This paper develops a multi-objective optimisation model to compute resource allocation,shelter assignment and routing options to evacuate late evacuees from affected areas to shelters.Three bushfire scenarios are analysed to incorporate constraints of restricted time-window and potential road disruptions.Capacity and number of rescue vehicles and shelters are other constraints that are identical in all scenarios.The proposed mathematical model is solved by ?-constraint approach.Objective functions are simultaneously optimised to maximise the total number of evacuees and assigned rescue vehicles and shelters.We argue that this model provides a scenario-based decision-making platform to aid minimise resource utilisation and maximise coverage of late evacuees

Significance of energy storages in future power networks

Alahakoon, S ORCiD: 0000-0002-5802-7869As a result of the major challenges the world is facing today due to global warming and the ever decreasing conventional sources of energy such as fossil fuels, developing methodologies for harnessing all possible forms of renewable energy has become a heavily researched area within the power and energy research communities. Deploying energy storages increases the possibilities of harnessing several sources of renewable energy in a more meaningful manner. Some of the key areas where energy storages could make things better, when it comes to harnessing renewable energy sources are, Wind energy, Bio energy, Geothermal energy, Solar energy and Wave energy. The paper investigates application examples of energy storages in these areas through a thorough review of reported scientific literature. On the other hand, major energy consuming areas such as transportation, manufacturing, electricity consumers etc. could also benefit by the introduction of energy storages. As an example, in transportation, increasing usage of hybrid electric vehicles, plug-in electric vehicles and emerging new concepts in transportation such as electric highways have raised the significant role of energy storage solutions for transportation to its highest level. It is believed that this way of looking at the energy storages will strategically position them with the significance they deserve within the energy and power engineering research community. © 2017 The Authors

Analysing stillbirth data using dynamic self organizing maps

Even with the presence of modern obstetric care, stillbirth rate seems to stay stagnant or has even risen slightly in countries such as England and has become a significant public health concern [1]. In the light of current medical research, maternal risk factors such as diabetes and hypertensive disease were identified as possible risk factors and are taken into consideration in antenatal care. However, medical practitioners and researchers suspect possible relationships between trends in maternal demographics, antenatal care and pregnancy information of current stillbirth in consideration [2]. Although medical data and knowledge is available appropriate computing techniques to analyze the data may lead to identification of high risk groups. In this paper we use an unsupervised clustering technique called Growing Self organizing Map (GSOM) to analyse the stillbirth data and present patterns which can be important to medical researchers

The design and construction of a battery electric vehicle propulsion system - high performance electric kart application

Alahakoon, S ORCiD: 0000-0002-5802-7869This paper presents an electric propulsion system designed specifically to meet the performance specification for a competition racing kart application. The paper presents the procedure for the engineering design, construction and testing of the electric powertrain of the vehicle. High performance electric Go-Kart is not an established technology within Australia. It is expected that this work will provide design guidelines for a high performance electric propulsion system with the capability of forming the basis of a competitive electric kart racing formula for Australian conditions. © Published under licence by IOP Publishing Ltd

An enhancing dynamic self-organizing map for data clustering

This paper presents a novel growing self-organizing map which features incremental learning, dynamic network structure and good visualization ability. It allows for on-line and continuous learning on both static and evolving data distributions. The experiments are carried out on some benchmark data sets for vector quantisation and clustering. Compared with the GSOM method, our results show that this new model can achieve better or comparable performance in real-world data sets

Evaluation of temperature rise in continuously loaded distribution earthing electrodes

© 2016 IEEE. This paper examines the relationship of current densities and temperature rise in continuously loaded earth electrodes as may be applied in Single Wire Earth Return (SWER) systems. The existing design practice for electrodes has been based on some fundamental calculations related to resistance and voltage rise and industrial experience. There is no comprehensive design method that relates the intensity of the electrode loading and the service life. An analytical model of a hemispherical electrode is established. The temperature rise is proportional to the soil thermal resistivity and electrical resistivity and the square of the electrode current. The final temperature will largely determine the loss of soil moisture which is a major factor in the failure of electrodes. The analytical model provides guidance as to current densities that are likely to result in long electrode lifetimes

Small signal modeling and control of isolated three port DC-DC converter for PV-battery system

The inclusion of battery energy storage systems (BESSs) with PV is technically attractive and is capable of solving intermittency and voltage issues. From the economic perspective, it may allow a consumer to shift their PV generation to satisfy their household loads. The addition of a battery to a home solar system requires an additional power converter. The traditional method for interconnecting PV-battery systems uses at least two separate power converters which results in multistage power conversion for some power flows. This paper proposes an isolated dc-dc three port converter (TPC) based on a dual active bridge (DAB) configuration for the PV-battery system and presents small signal modeling and power flow control of the proposed TPC using a circuit averaging approach. The TPC will interface a PV, a rechargeable battery and a load. Two degrees of freedoms (DOFs) are required to control the instantaneous power of three ports simultaneously. As a result, two controllers are designed for port 2 and port 3 (i.e. PV and load) while port 1 (battery) is responsible for power balance. The phase shift modulation (PSM) technique is used to generate the phase shifted converter voltages to facilitate the power transfer between the ports. The simulation results show that the controllers are capable of independently regulating the instantaneous power flow between the three ports

Three-port converter with decoupled power control strategies for residential PV-battery system

Alahakoon, S ORCiD: 0000-0002-5802-7869; Wolfs, PJ ORCiD: 0000-0001-7048-1231The traditional method for interconnecting PV and a battery energy storage system (BESS) to a household load uses at least two separate converters. In this paper, a fully-isolated three-port converter (TPC) which is based on a dual active bridge (DAB) configuration is proposed for integrating the PV and BESS with the household loads to avoid multistage power conversion. A converter with more than two ports is normally designed to have multimode operations which requires multiple control loops. Due to these multiple cross-coupled power control loops, designing closed loop power control strategies for the TPC becomes a complex issue. This paper presents the small signal modeling of the isolated TPC with proper decoupled networks to eliminate the interaction between multiple power control loops. The decoupled network allows a closed loop power control system design for the TPC to control the port powers independently. As two degrees of freedom (DOFs) are available, the control system can control the PV port power and the ac load port power. The battery port must then provide the balancing power. The power flows between the ports are managed by phase shift modulation (PSM). The responses of the power control system of the proposed TPC with decoupled networks are verified by simulation studies