Developing models of streaming system performance is of critical operational importance. Achieving representative coverage of potential operational conditions commonly requires large-scale experimentation which can be time-consuming and resource-intensive. Existing approaches to experimental design found in the literature typically scale poorly to the range of operating conditions required to provide robust experimental findings. This paper explores the potential of a class of approaches used commonly in other disciplines, namely Response Surface Methodology. The work presents an empirical analysis of the effectiveness of three RSM approaches, 2kFactorial, Central Composite and Box-Behnken Designs. We further evaluate the effectiveness of a suite of goodness-of-fit methods to quantify performance degradation in streaming performance, in order to understand the interplay between experimental design and choice of metric. We provide insights into combinations of experimental design and evaluation metric which we believe to be instructive to system operators. We make our implementation available to support replication efforts and support the uptake of these methods more broadly in the community.

Graph representation plays a key role in graph analytics to perform a variety of downstream machine-learning tasks. This paper presents a novel method for extracting expressive graph representation based on a combination of statistics captured from a graph and node properties. We use both local and global-level information along with the original node properties to extract a meaningful feature representation of the graph. This allows us to build expressive graph descriptors that can be run with limited training data and computational resources and achieve competitive results. We discuss the merits of the proposed approach in terms of sensitivity, running times, and stability. Our evaluation of various graph classification benchmark datasets shows that the proposed method either outperforms or provides similar results to state-of-the-art methods. We further outline the potential future directions in graph machine learning research.

A lowering in the cost of batteries and solar PV systems has led to a high uptake of solar battery home systems. In this work, we use the deep deterministic policy gradient algorithm to optimise the charging and discharging behaviour of a battery within such a system. Our approach outputs a continuous action space when it charges and discharges the battery, and can function well in a stochastic environment. We show good performance of this algorithm by lowering the expenditure of a single household on electricity to almost $1AUD for large batteries across selected weeks within a year.

The electricity market has a vital role to play in the decarbonisation of the energy system. However, the electricity market is made up of many different variables and data inputs. These variables and data inputs behave in sometimes unpredictable ways which can not be predicted a-priori. It has therefore been suggested that agent-based simulations are used to better understand the dynamics of the electricity market. Agent-based models provide the opportunity to integrate machine learning and artificial intelligence to add intelligence, make better forecasts and control the power market in better and more efficient ways. In this systematic literature review, we review 55 papers published between 2016 and 2021 which focus on machine learning applied to agent-based electricity market models. We find that research clusters around popular topics, such as bidding strategies. However, there exists a long-tail of research applications that could benefit from the high intensity research from the more investigated applications.

Failure prediction of hard disks has garnered significant interest from the research community in recent years. Several prior studies have leveraged operational datasets from large cloud storage providers to develop failure models which have been shown to exhibit good predictive accuracy. However, these datasets are highly imbalanced, with relatively scarce data concerning failed drives. In this paper, we set out to develop accurate predictions leveraging only commonly used S.M.A.R.T. attributes as predictors and to explore the impact of various imbalance mitigation strategies on our predictive ability. We leverage open data from Backblaze in developing an LSTM-based model which achieves a Matthews Correlation Coefficient of 0.71. We demonstrate the potential of more universally applicable models, portable to new operational datasets and disk types.

Decentralized electricity markets are often dominated by a small set of generator companies who control the majority of the capacity. In this paper, we explore the effect of the total controlled electricity capacity by a single, or group, of generator companies can have on the average electricity price. We demonstrate this through the use of ElecSim, a simulation of a country-wide energy market. We develop a strategic agent, representing a generation company, which uses a deep deterministic policy gradient reinforcement learning algorithm to bid in a uniform pricing electricity market. A uniform pricing market is one where all players are paid the highest accepted price. ElecSim is parameterized to the United Kingdom for the year 2018. This work can help inform policy on how to best regulate a market to ensure that the price of electricity remains competitive.We find that capacity has an impact on the average electricity price in a single year. If any single generator company, or a collaborating group of generator companies, control more than ~11% of generation capacity and bid strategically, prices begin to increase by ~25%. The value of ~25% and may vary between market structures and ~11% countries. For instance, different load profiles may favour a particular type of generator or a different distribution of generation capacity. Once the capacity controlled by a generator company, which bids strategically, is higher than ~35%, prices increase exponentially. We observe that the use of a market cap of approximately double the average market price has the effect of significantly decreasing this effect and maintaining a competitive market. A fair and competitive electricity market provides value to consumers and enables a more competitive economy through the utilisation of electricity by both industry and consumers.