Renewable Energy Resources Impact on Clean Electrical Power by developing the North-West England Hydro Resource Model.

This paper describes the development of a sequential decision support system to promote hydroelectric power in North-West England. The system, composed of integrated models, addresses barriers to the installation of hydroelectric power schemes. Information is linked through an economic assessment which identifies different turbine options, assesses their suitability for location and demand; and combines the different types of information in a way that supports decision making. The system is structured into five components: the hydrological resource is modelled using Low Flows 2000, the turbine options are identified from hydrological, environmental and demand requirements; and the consequences of different solutions will be fed into other components so that the environmental impacts and public acceptability can be assessed and valued. A preliminary case study is presented on an old gunpowder works to illustrate how the resource model may be employed. Historical architectural structures, power uptake and educational instruction of hydro power technology are considered

Mastering DeepRTS with Transformers

The Transformer deep learning model has recently proven its superiority in tasks like natural language processing and computer vision, as tools like ChatGPT and DALL-E have become widespread and helps humans complete tasks faster with high accuracy. This comes from the ability of Transformer models to comprehend sequential data by weighing the importance of each token in sequences through an attention mechanism and being trained on massive amounts of data. As researchers seek to apply Transformer models to other disciplines, the sequential nature of reinforcement learning tasks becomes an interesting study area. Despite the demonstrated superiority of transformer-based models in various domains, their adoption within reinforcement learning paradigms, particularly within game-based learning environments, has yet to become widespread. In particular, reinforcement learning problems where an intelligent agent learns how to act in a video game are interesting, as they can help simulate real-life scenarios and therefore make autonomous systems less expensive and safer to train. Real-Time Strategy games are complex video games where players must develop a strategy in real-time to gain an advantage over other players, and reaching game objectives often involve performing a specific sequence of actions, making them an excellent area of study for reinforcement learning combined with Transformers. This thesis explores, evaluates and improves Transformer models applied in Real-Time Strategy Game environments with a particular focus on limited data and computational power resources. To this end, DeepRTS is chosen as the reinforcement learning environment for its high performance and simplified game mechanics, but also to enrich its relatively small research domain. This work implements several sub-environments in DeepRTS with various objectives and levels of complexity to give agents a diverse range of tasks and to compare deep learning algorithms. The authors of this thesis also contributed to the DeepRTS project by fixing source code issues to improve performance. As there is no publicly available dataset for DeepRTS to train a Transformer model on, this thesis proposes a novel model, namely the Genetic Algorithm Decision Transformer, a new implementation for data generation in reinforcement learning environments by leveraging the autoregressive Decision Transformer model for action prediction. The novelty lies in using the genetic algorithm to select the best data samples from a pool to train a Decision Transformer agent. Results are compared against a Double Deep Q-learning agent and a standard Decision Transformer agent, the latter being trained using different datasets, and results show its dependency on high-quality data. Genetic Algorithm Decision Transformer improves the aforementioned algorithms by generating its own dataset with high-quality data samples while using the same underlying Decision Transformer model. Results show that Genetic Algorithm Decision Transformer outperforms its counterpart Decision Transformer algorithm by a magnitude of up to 3.3 times the reward. However, improvements to data collection could improve the model further

Advancing Alternative Analysis: Integration of Decision Science.

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.Assess whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings.We conclude the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients, and would also advance the science of decision analysis.We advance four recommendations: (1) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts