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There is an ever-growing number of photovoltaic (PV) installations in the US and worldwide. Many utilities do not have complete or up-to-date information of the PVs present within their grids. This research presents a deep neural network approach for estimating PV size, tilt, and azimuth using only behind-the-meter data. It is found that the proposed deep neural network (DNN) method can estimate PV size with an error of 2.09% in a data set with fixed tilt and azimuth values and 3.98% in a data set with varying tilt and azimuths. This is a lower error than the benchmark linear regression approach. A net load data resolution of 1 min provides the lowest error when estimating the PV size. The proposed DNN is also reasonably robust to erroneous training data. When applied to estimate PV tilt and azimuth, the proposed method achieves a mean absolute percentage error of 10.1% and 2.8% respectively. These error metrics are 2.0 × and 3.7 × lower, respectively, than the benchmark linear regression achieves. It was observed that a higher data resolution (1 min) does not provide significant gains in accuracy. It is recommended that a data resolution of 60 min is used to reduce the effects of phenomena such as cloud enhancements. The proposed deep neural network approach is also highly robust, maintaining reasonable accuracy with high levels of mislabeled training data.

Buildings are the highest consumers of electrical energy globally. Developing effective control strategies to enhance the operation of buildings to increase their energy efficiency has therefore become a very active area of research. This research proposes the use of evolutionary neural networks for the task of Heating Ventilation and Air Conditioning control. A neural network controller is trained using the exponential Natural Evolutionary Strategy algorithm is proposed. Its performance is bench-marked against multiple state of the art control strategies, including: a Genetic Algorithm trained neural network, the popular Deep Q-Network reinforcement learning algorithm and also a threshold based control policy. The results presented demonstrate that evolutionary neural networks are an effective strategy for reducing energy consumption while also minimizing thermal discomfort. The energy consumption and thermal discomfort achieved by evolutionary neural networks, are significantly lower than when using Deep Q-Network. This is particularly true for the Natural Evolutionary Strategy algorithm.

Increased penetration of behind-the-meter (BTM) PV installations can cause numerous challenges in planning and operation of distribution systems. Utilities must accurately record the installed PVs in their territory and keep their PV database updated. However, many utilities do not have enough visibility on the actual installed PVs due the growing number of unauthorized PV installations as well as the complexity of data tracking and updating the databases even for authorized PVs. In this paper, a data-driven classification method is proposed for detecting BTM PV installation using convolutional neural networks and synthetic net load profiles generated from AMI data. The network is trained and tested on 50 folds of the dataset and the testing classification accuracy per each fold is calculated. Results show that the median of per-fold testing accuracies is 98.9%. In terms of average error, only 0.7% of the customers with PV are not detected. This is significantly less than the 6% error in the next best method. The impact of training data parameters, such as the size of dataset and label errors on the accuracy and computational time of the method is also studied and characterized. Using only the available AMI data, the proposed method can help utilities accurately monitor BTM PV systems and keep their databases updated and thus avoid the costs of operation and planning errors.

The Infrastructure as a Service (IaaS) platform in cloud computing provides resources as a service from a pool of compute, network, and storage resources. One of the major challenges facing cloud computing is to predict the usage of these resources in real time. By knowing future demands, cloud data centres can dynamically scale resources to decrease energy consumption while maintaining a high quality of service. However cloud resource consumption is ever changing, making it difficult for accurate predictions to be produced. This motivates the research presented in this paper which aims to predict in advance the level of CPU consumption of a host. This research implements evolutionary Neural Networks (NN), a powerful machine learning method, to make these predictions. A number of state of the art swarm and evolutionary optimization algorithms are implemented to train the neural networks to predict host utilization: Particle Swarm Optimization (PSO), Differential Evolution (DE) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). The results of this research demonstrate that CMA-ES converges faster to a better solution on the training data. However when evaluated on the test data, DE performs statistically equal to CMA-ES. The results also demonstrate that the trained networks are still accurate when applied to CPU utilization data from different hosts with no further training needed. When evaluated to predict multiple steps into the future, the accuracy of the network understandably decreases but still performs well on average.

Multi-objective optimisation has received considerable attention in recent years as many real world problems have multiple conflicting objectives. There is an additional layer of complexity when considering multi-objective problems in dynamic environments due to the changing nature of the problem. A novel Multi-Objective Neural Network trained with Differential Evolution (MONNDE) is presented in this research. MONNDE utilizes Neural Network function approximators to address dynamic multi-objective optimisation problems. Differential Evolution (DE) is a state of the art single objective global optimisation problems and will be used to evolve neural networks capable of generating Pareto fronts. The proposed MONNDE algorithm has the added advantage of developing an approximation of the problem that can produce further Pareto fronts as the environment changes with no further optimisation needed. The MONNDE framework is applied to the Dynamic Economic Emission Dispatch (DEED) problem and performs equally optimal when compared to other state of the art algorithms in terms of the 24 h cost and emissions. This research also compares the performance of fully and partially connected networks and discovers that dynamically optimising the topology of the neural networks performs better in an online learning environment than simply optimising the network weights.

Neuroevolution refers to evolving neural networks using evolutionary methods. These algorithms have been applied to many problem domains, from game playing to robotics, which motivates this research. The problem of watershed management is addressed here in this research using the most prominent neuroevolution algorithms, i.e. NeuroEvolution of Augmenting Topologies (NEAT), neuro differential evolution and Enforced SubPopulations . The results indicate that neuroevolution is a suitable approach at addressing the watershed management problem, outperforming the other methods of neural network training.

The ability to accurately predict future power demands, power available from renewable resources and the environmental impact of power generation is vital to the energy sector for the purposes of planning, scheduling and policy making. Machine learning techniques, neural networks in particular, have proven to be very effective methods for addressing these challenging forecasting problems. This research utilizes the powerful evolutionary optimisation algorithm, covariance matrix adaptation evolutionary strategy, as a means of training neural networks to predict short term power demand, wind power generation and carbon dioxide intensity levels in Ireland over a two month period. The network is trained over one month and then tested over the following month. A neural network trained with covariance matrix adaptation evolutionary strategy performs very competitively when compared to other state of the art prediction methods when forecasting Ireland's energy needs, providing fast convergence, more accurate predictions and robust performance. The covariance matrix adaptation evolutionary strategy trained network also gives accurate predictions when predicting multiple time steps into the future.

Particle swarm optimisation (PSO) is a general purpose optimisation algorithm used to address hard optimisation problems. The algorithm operates as a result of a number of particles converging on what is hoped to be the best solution. How the particles move through the problem space is therefore critical to the success of the algorithm. This study utilises meta optimisation to compare a number of velocity update equations to determine which features of each are of benefit to the algorithm. A number of hybrid velocity update equations are proposed based on other high performing velocity update equations. This research also presents a novel application of PSO to train a neural network function approximator to address the watershed management problem. It is found that the standard PSO with a linearly changing inertia, the proposed hybrid Attractive Repulsive PSO with avoidance of worst locations (AR PSOAWL) and Adaptive Velocity PSO (AV PSO) provide the best performance overall. The results presented in this paper also reveal that commonly used PSO parameters do not provide the best performance. Increasing and negative inertia values were found to perform better.

Particle swarm optimisation (PSO) is a bio-inspired swarm based approach to solving optimisation problems. The algorithm functions as a result of particles traversing and evaluating the problem space, eventually converging on the optimum solution. This paper applies a number of PSO variants to the dynamic economic emission dispatch (DEED) problem. The DEED problem is a multi-objective optimisation problem in which the goal is to optimise two conflicting objectives: cost and emissions. The PSO variants tested include: the standard PSO (SPSO), the PSO with avoidance of worst locations (PSO AWL), and also a selection of different topologies including the PSO with a gradually increasing directed neighbourhood (PSO GIDN). The aim of the paper is to test the performance of different variants of the PSO AWL against variants of the SPSO on the DEED problem. The results show that the PSO AWL outperforms the SPSO for every topology implemented. The results are also compared to state of the art genetic algorithm (NSGA-II) and multi-agent eeinforcement learning (MARL). This paper then examines the performance of each PSO algorithm when the power demand is modified to form a triangle wave. The purpose of this experiment was to analyse the performance of different PSO variants on an increasingly constrained problem.

The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management. This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems. The main direction for future research and challenges in reinforcement learning are also outlined., 17 pages, 3 figures

Collecting, analyzing and gaining insight from large volumes of data is now the norm in an ever increasing number of industries. Data analytics techniques, such as machine learning, are powerful tools used to analyze these large volumes of data. Synthetic data sets are routinely relied upon to train and develop such data analytics methods for several reasons: to generate larger data sets than are available, to generate diverse data sets, to preserve anonymity in data sets with sensitive information, etc. Processing, transmitting and storing data is a key issue faced when handling large data sets. This paper presents an "On the fly" framework for generating big synthetic data sets, suitable for these data analytics methods, that is both computationally efficient and applicable to a diverse set of problems. An example application of the proposed framework is presented along with a mathematical analysis of its computational efficiency, demonstrating its effectiveness., Comment: 13 pages, 3 figures

One of the major challenges facing cloud computing is to accurately predict future resource usage for future demands. Cloud resource consumption is constantly changing, which makes it difficult for forecasting algorithms to produce accurate predictions. This motivates the research presented in this paper which aims to predict host machines CPU consumption for a single time-step and multiple time-steps into the future. This research implements a Recurrent Neural Network to predict CPU utilisation, due to their ability to retain information and accurately make predictions for time series problems, making it a promising candidate to predict CPU utilization with greater accuracy when compared to traditional approaches.

Reinforcement Learning (RL) is a powerful and well-studied Machine Learning paradigm, where an agent learns to improve its performance in an environment by maximising a reward signal. In multi-objective Reinforcement Learning (MORL) the reward signal is a vector, where each component represents the performance on a different objective. Reward shaping is a well-established family of techniques that have been successfully used to improve the performance and learning speed of RL agents in single-objective problems. The basic premise of reward shaping is to add an additional shaping reward to the reward naturally received from the environment, to incorporate domain knowledge and guide an agent’s exploration. Potential-Based Reward Shaping (PBRS) is a specific form of reward shaping that offers additional guarantees. In this paper, we extend the theoretical guarantees of PBRS to MORL problems. Specifically, we provide theoretical proof that PBRS does not alter the true Pareto front in both single- and multi-agent MORL. We also contribute the first published empirical studies of the effect of PBRS in single- and multi-agent MORL problems.

This research presents a novel framework for evolving Multi-Objective Neural Networks using Differential Evolution (MONNDE). In recent years, the Differential Evolution algorithm has shown to be an effective and robust global optimisation algorithm. The algorithm uses evolutionary operators to optimise complex and continuous problem spaces and has been applied to a range of problems, recently including neural networks. This research continues this trend by utilizing differential evolution to evolve neural networks capable of addressing dynamic problems with multiple objectives. The proposed MONNDE framework is applied to the Dynamic Economic Emission Dispatch (DEED) problem. This problem consists of scheduling a group of power generators in a manner that minimises both cost and emissions produced by the generators. The power generators must also meet a series of constraints relating to their power output, power demand and network loss. The proposed MONNDE is performs very competitively when compared to algorithms such as NSGA-II, PSO, PSOAWL and MARL.

Particle swarm optimisation (PSO) is an optimisation algorithm in which particles traverse a problem space moving towards promising locations which either they or their neighbours have previously visited. This paper presents a new PSO variant with the Avoidance of Worst Locations (AWL). This variation was inspired by animal behaviour. In the wild, an animal will react to negative stimuli as well as positive, e.g. an animal looking for food will also be conscious of danger. PSO AWL enables particles to remember previous poor solutions as well as good. As a result, the particles change the way they move and avoid known bad areas. Balancing the influence of these poor locations is vital. The research in this paper found that a small influence from bad locations on the particles leads to a significant improvement on overall performance when compared to the standard PSO. When compared to previous implementations of worst location memory, PSO AWL demonstrates vast improvements.