Across all experiments, Hyperseed convincingly machine learning and robotics context is currently gaining a demonstrates its key novelties of learning from a few input great momentum [1]-[6]. In classification tasks, the use of vectors and single vector operation learning rule, both of which VSA leads to order of magnitude increase in energy efficiency contribute towards reduced time and computation complexity. of computations on the one hand and natively enables oneshot The paper is structured as follows. Section II describes and multitask learning on the other [7]. It is prospected the related work relevant to Hyperseed operations. The used that VSA will play a key role in the development of novel methods including the fundamentals of VSA are presented neuromorphic computer architectures [8] as an algorithmic in Section III. Section IV presents the main contribution - abstraction [9], [10]. The main contribution of this paper is the method for unsupervised learning Hyperseed. Section V a novel algorithm for unsupervised learning called Hyperseed, reports the results of the performance evaluation the experiments.

The global ambitions of a carbon-neutral society necessitate a stable and robust smart grid that capitalises on frequency reserves of renewable energy. Frequency reserves are resources that adjust power production or consumption in real time to react to a power grid frequency deviation. Revenue generation motivates the availability of these resources for managing such deviations. However, limited research has been conducted on data-driven decisions and optimal bidding strategies for trading such capacities in multiple frequency reserves markets. We address this limitation by making the following research contributions. Firstly, a generalised model is designed based on an extensive study of critical characteristics of global frequency reserves markets. Secondly, three bidding strategies are proposed, based on this market model, to capitalise on price peaks in multi-stage markets. Two strategies are proposed for non-reschedulable loads, in which case the bidding strategy aims to select the market with the highest anticipated price, and the third bidding strategy focuses on rescheduling loads to hours on which highest reserve market prices are anticipated. The third research contribution is an Artificial Intelligence (AI) based bidding optimization framework that implements these three strategies, with novel uncertainty metrics that supplement data-driven price prediction. Finally, the framework is evaluated empirically using a case study of multiple frequency reserves markets in Finland. The results from this evaluation confirm the effectiveness of the proposed bidding strategies and the AI-based bidding optimization framework in terms of cumulative revenue generation, leading to an increased availability of frequency reserves.