The increasing demand for electricity has lead to demand for more efficient energy production. One promising option is wind power, which currently provides an estimated 7.8% of the world’s energy production. One of the problems with wind energy is that a small percentage of the energy is lost due to the wake effect. The wake of a wind turbine is an area of low wind speed and high turbulence which is caused by the spinning of the turbine. This wake effect can mitigated by active wake control, which is a process by which the wake from a turbine is redirected away from downwind turbines, by changing the yaw of the turbine head. Calculating a policy for doing this is computationally expensive to do using numerical optimisation. Therefore, multi agent reinforcement learning is proposed to learn a policy which performs active wake control.
The proposed approach makes use of the popular reinforcement learning algorithm REINFORCE, and extends it using a variety of methods. First, a simplified version of the problem is treated, wherein the wind direction is fixed. Then the problem is made more realistic by introducing changing wind directions. The first extension of REINFORCE that is treated is difference rewards, a reward shaping strategy which seeks to solve the credit assignment problem, thereby improving cooperation between turbines. The second method uses training regimes, which train different agents at different times to stabilise the environment as much as possible. Next, role-based reinforcement learning is used to conteract the complexity of the problem by allowing each agent to specialise for a certain role. Finally, since roles cannot be manually determined for larger farms, influence-based abstraction is used to enable agents to learn the roles themselves, by abstracting spacial information and presenting it to the agent as an observation.
The results demonstrate that multi agent reinforcement learning can be used to perform active wake control in wind farms. Furthermore, the extensions proposed are shown to improve learning, and lead to greater energy output. While multi agent reinforcement learning is shown to be a promising way to tackle active wake control in wind farms, research is needed to improve the stability of the learned policies. ... Read more

The key to producing high-fidelity time-series data is to preserve temporal dynamics. This means that generated sequences respect the relationship between variables across time as in the original data. While new types of GANs have been used to generate time-series data, they, like previous GAN
implementations, are time consuming to train. A novel federated framework is proposed, which generates realistic time-series data, by combining supervised and unsupervised training. The framework is based on the work in TimeGAN and Federated GAN (FeGAN). Using an embedded learning space, TimeGAN
encourages the network to mimic the structure of the training data. FeGAN allows the results of TimeGAN to be combined at a central server, which has benefits for both throughput, and potential to improve data privacy. This also introduces the possibility of using cross domain data. The challenge with creating applying federated learning to TimeGAN, and timeseries data in general is whether the learned temporal dynamics can be combined. This is accomplished by the combination of the weighting and sampling scheme used. This paper demonstrates, by qualitative and quantitative analysis, the ability novel framework proposed, to produce equivalent quality synthetic timeseries data compared to the original TimeGAN, without sharing local data between nodes in the network. This is based on the predictive and discriminative scores described, as well as PCA and t-SNE analysis. Additionally, there is an approximate eleven percent increase in Floating Point Operations per second when using one machine, and up to a thirty percent increase when using multiple. ... Read more