Solar photovoltaics are on track to becoming the largest renewable energy source by 2029. This means a rapid increase in the number of solar energy generation installations from residential roof-top systems to utility-scale power plants. The current industrial approaches towards predictive maintenance will be insufficient to manage and maintain the increasing numbers of such installations at peak performance. In this paper, we propose hyper-predictive maintenance as a novel approach based on Generative Artificial Intelligence (AI) agents for highly autonomous management of solar energy infrastructure. The proposed Agentic AI framework deploys multiple agents for baseline generation from solar installations, predictive model development, degradation estimation, degradation evaluation and predictive maintenance that combines baseline performance with contextual information to predict faults and potential causes. This framework is empirically evaluated in the real-world solar energy systems of a multi-campus tertiary education institution. The results of these experiments confirm the robust and accelerated performance of Generative AI agents for the hyper predictive maintenance of large-scale solar energy installations.

Contemporary energy platforms are leveraging advanced data management and Artificial Intelligence (AI) capabilities in response to the increasing complexity of energy systems and grids. Despite these advances, it is a non-trivial and challenging task to support the decision-making needs of the human operators of such complex energy-related implementations. Conversational agents or chatbots are a potential emerging technology that can be utilized to address this challenge. Although there is a large body of literature on chatbots in general, they are not robust as they rely on predefined conversational pathways that are inadequate to efficiently address the complexities of dynamic data spaces in energy platforms. The capability of generating answers in real-time by communicating with the dynamic dataspace is crucial as energy management decisions are real-time and time sensitive. In this paper, we present the design and development of Cooee, a chatbot for conversational engagement with the dynamic data spaces of complex energy environments. Cooee leverages state-of-art language models along with rule-based language processing methods for a conversational interaction with dynamic data spaces, which consequently supports and enables decision-making by human experts. We have developed Cooee as a standalone application and then integrated into a real-world energy AI platform deployed within a multi-campus tertiary education institution setting. Cooee was empirically evaluated in this setting and compared with several state-of-the-art Q&A approaches.

The rapid advancement of the energy sector in terms of diverse energy generation options and increasing energy consumption loads has eventuated the need for highly accurate demand forecasting methods. The prevalence of large volumes of energy data streams and sophisticated Artificial Intelligence (AI) algorithms has enabled a rapid transition to AI-based forecasting methods that are more accurate and computationally efficient. Despite this transition, demand forecasting during peak events and peak temporal periods continues to be a challenge due to the irregularity and transience of such events. Besides the challenge of managing supply and demand, the financial viability of forecasting is also questioned when the forecast decreases in accuracy during peak periods when the energy price is an increasing function. In this paper, we have set out to address the challenge of peak demand forecasting by specifically transforming both input vectors and input attributes of the smart meter data streams. Input vectors are transformed using active thresholding while input attributes are transformed into a feature subset using model explainability. We have evaluated the effectiveness of this data transformation on the current state-of-the-art AI for energy demand forecasting, gradient boosting. We conduct a comparative evaluation using two real-world energy consumption datasets drawn from the La Trobe Energy AI/Analytics Platform (LEAP), of La Trobe University’s Net Zero Carbon Emissions Program. The proposed approach surpasses the baseline approach in both datasets, with an improvement of 27% for the second dataset which is a high energy consumption setting.

Microgrids and energy platforms have become increasingly intricate in nature. This is especially true in platforms adopted in large multi functional complexes which are spread across geographies of varying climactic conditions. Many of these complexes have evolved over time as organisations have grown and diversified, this has resulted in a mix of infrastructure, technology, networks, systems and equipment woven in an inextricable web. Often managing such complex hierarchies involve interacting with heterogeneous management platforms from various vendors and built on different platforms each providing the solution to a piece of the ’energy puzzle’ of the organisation. The complexities involved in managing such a network of systems are manifold and require extensive resourcing and expertise. Sustainability and Net Zero carbon emission initiatives that aim to achieve international and national targets add a further layer of complexity to the task at hand. In this paper, we propose a Cloud Edge architecture that leverages Artificial Intelligence (AI) and data analytics for microgrid energy optimisation and net zero carbon emissions. This architecture provides an intelligent and cohesive abstraction to assist in cataloging, unifying and managing the complexities of microgrids and enabling sustainable management of energy. The proposed architecture has been operationalised as the energy management and optimisation platform at a multi-campus, multi-functional tertiary education institution. Empirical evaluations conducted on this deployment have generated results that confirm the function and effectiveness of this architecture in addressing the emerging and evolving challenges of microgrid energy optimisation and net zero carbon emissions.

In this paper we introduce UNICON, a large-scale open dataset on UNIversity CONsumption of utilities, electricity, gas and water. This dataset is publicly released as part of La Trobe University’s commitment to Net Zero Carbon Emissions by 2029, for which we are building the La Trobe Energy AI/Analytics Platform (LEAP) that leverages Artificial Intelligence (AI) and Data Analytics to analyse, predict and optimize the consumption, generation and utilization of electricity, renewables, gas and water resources. UNICON contains consumption data for La Trobe’s five campuses in geographically distributed regions, across four years, 2018-2021 inclusive. This includes the COVID-19 global pandemic timeline of university shutdown and work from home measures that led to a significant decrease in the consumption of utilities. The consumption data consists of smart electricity meter readings at 15-minute granularity, gas meter readings at hourly intervals and water meter readings at 15-minute intervals. UNICON also contains weather data from the closest weather station to each campus, collected at two-speed latency of 1 minute and 10 minutes. The dataset is annotated with internal events of significance, such as energy conservation measures (ECMs) and other measurement and validation (M&V) activities conducted as part of LEAP optimization. To the best of our knowledge, this is the first large-scale, comprehensive, open dataset for the three main utilities, electricity, gas, and water consumption in a multi-campus university setting. A high granularity data dictionary and technical validation of the dataset for consumption trends, baseline modelling and forecasting are further contributions of this article that will enable interested research scientists, academics, industry practitioners, sustainability and energy consultants to experiment and evaluate their AI algorithms, models, forecasts, as well as inform the development of energy benchmarks, guidelines and much needed data-driven energy policies.

Time series forecasting is a critical requirement for the optimal operation of energy grids, systems, and platforms, where the forecasting challenge itself can span across energy consumption, renewables generation, and energy utilisation. Artificial Intelligence (AI) algorithms and models have been leveraged to predict these time series forecasts with increasing levels of accuracy. In contrast to local models that are developed separately for each time series, Global Models, which are trained across many sets of time series drawing on characteristics of ’relatedness’, have produced more accurate forecasts. In this paper, we propose a transformer architecture based global model as a generalist forecaster of energy time series data, where we frame a sequence forecasting model and represent numerical values of the corresponding time series as vector embeddings in this model. We evaluate this transformer architecture based global model on real-world time-series energy data generated by the La Trobe Energy AI platform (LEAP), a functional and operational microgrid deployed in the multicampus tertiary education setting of La Trobe University, Australia. The results of these experiments confirm that the proposed generalist forecasting approach outperforms specialist local models trained on individual time series. We also demonstrate the ability of this approach to forecast dissimilar time series from the same model.

We introduce an open dataset of high-granularity Photovoltaic (PV) solar energy generation, solar irradiance, and weather data from 42 PV sites deployed across five campuses at La Trobe University, Victoria, Australia. The dataset includes approximately two years of PV solar energy generation data collected at 15-minute intervals. Geographical placement and engineering specifications for each of the sites are also provided to aid researchers in modelling solar energy generation. Weather data is available at 1-minute intervals and is provided by the Australian Bureau of Meteorology (BOM). Apparent temperature, air temperature, dew point temperature, relative humidity, wind speed, and wind direction were provided under the weather data. The paper describes the data collection methods, cleaning, and merging with weather data. This dataset can be used to forecast, benchmark, and enhance operational outcomes in solar sites.

In this paper we have introduced a mobility prediction scheme that proposes the use of a new sector-based tracking of mobile users, with a sector-numbering scheme to predict user movements. The proposed scheme is applicable for both the fixed network and the ad hoc networking structures. Our study shows that accurate prediction is possible with reduced area of tracking for both types of networks.

This is a case study on using a DSP board to construct an encryption/decryption module embedded in a e-commerce Web server. The idea of using DSP is to push beyond the key length limits of encryption/decryption algorithms and computational power in a software environment while avoiding the heavy investment in a dedicated hardware encryptor/encryptor. The low cost, high computational power, high flexibility of DSP and the ubiquitous availability of a PC peripheral component interconnect slot for the DSP can provide any Web browser or Web server an excellent cost-effective option to improve the security level of Internet applications. The paper provides a step-by-step procedure and reveals every detail of a successful implementation of a DSP RSA encryptor/decryptor for an e-commerce Web server by using the latest TMS320C6000/sup TM/ Evaluation Module (EVM) DSP hardware. A strong prime concept and Garner algorithm are introduced to generate more secure keys and compute encryption/decryption more efficiently than that of recent publications. Experiments show that the performance of using DSP hardware encryption can be 300 times faster than that in software environment.

A baud-rate full-duplex digital transmission unit for 144-kb/s basic access over an existing subscriber loop plant is described. The adaptive operations performed include echo cancellation, decision feedback equalization, reference control, and timing recovery. The unit operates at the baud rate, allows a fully digital implementation, and requires no special training sequences. Baud-rate timing recovery is a crucial issue. A method which only estimates timing error when certain data sequences occur is described and applied to subscriber loops. The behavior of this method in a region of incorrect decisions due to intersymbol interference is analyzed as a one-dimensional random walk. This indicates that escape from this region will be rapid when the gain is moderately large. Timing issues at exchange and subscriber ends are discussed. Different start-up strategies involving staggered turn-on plus gear shifting for both ends are proposed to decouple the adaptive loops and yield relatively low convergence times. In particular, at the exchange end, timing recovery is initially decoupled by successively adapting the remaining loops at several instants within the baud period. The instant closest to the optimum is then that instant with a minimum timing estimate variance.>