Music generation using deep learning has received considerable attention in recent years.Researchers have developed various generative models capable of cloning musical conventions, comprehending the musical corpora, and generating new samples based on the learning outcome. Although the samples generated by these models are persuasive, they often lack musical structure and creativity. Moreover, a vanilla end-to-end approach, which deals with all levels of music representation at once, does not offer human-level control and interaction during the learning process, leading to constrained results. Indeed, music creation is a recurrent process that follows some principles by a musician, where various musical features are reused or adapted. On the other hand, a musical piece adheres to a musical style, breaking down into precise concepts of timbre style, performance style, composition style, and the coherency between these aspects. Here, we study and analyze the current advances in music generation using deep learning models through different criteria. We discuss the shortcomings and limitations of these models regarding interactivity and adaptability. Finally, we draw the potential future research direction addressing multi-agent systems and reinforcement learning algorithms to alleviate these shortcomings and limitations.

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This paper provides a method for determining the economic incentives and limitations for a battery used for peak clipping, with the goal of finding an optimal mix between the battery’s power density and energy density. A ratio called the R-factor has been introduced, which helps determine the energy demand to curb the peak. The paper’s results embrace different investment scenarios showing what battery capacity can be expected, dependent on interest rates, payback time and potential savings in power tariffs due to curtailment. In addition, the paper introduces the “wrench and cut” concept, which can help improve the investment case for batteries by combining battery operations with standard demand response operations. In particular, the effect of using a limited form of demand response-based load deactivation together with a battery has been analyzed. The investigation provided raises a point that battery degradation must be taken into account to prevent the reduction of battery life and possibly the needed payback period. The ultimate target of the presented research refers to vehicle-to-grid/vehicle-to-building developments in the Arctic region, where a vehicle is considered a mobile battery and where flexibility can be delivered in a cost-efficient way.

The research presented here has been conducted in the Smart Charge project. It has addressed the use of renewables, e-mobility and battery charging in the Arctic as part of an effort to solicit fossil-fuelled alternatives. Of particular interest has been to determine what impact and support electric snowmobiles can provide together with local, renewable energy production. The relevance of vehicle-togrid/ building (V2G/B) solutions have been investigated in the project too. The idea has been to use electric snowmobiles for load shaving during extensive periods of the year. The research has looked at cost aspects, value stacking, climate impact as well as aggregated effects of controlled fleet management of idle snowmobiles. A case study undertaken at Longyearbyen at Svalbard, Norway has provided the most important empirical basis for the research presented. The research concludes that electric snowmobiles can have a positive effect on the local energy system and despite limited range can be quite attractive for the individual to operate if energy for charging is based on local driving solar power.