The project energy Optimization of building Internet Of Things Infrastructures in a Stratified way presented a holistic approach for an AI-enabled EMS for building-level energy management and max- imizing renewable energy sources utilization. The solution integrated a Photovoltaics (PV) generation forecasting module, a building energy demand forecasting module, and an energy task scheduling and optimization component. This paper presents the methodology and preliminary results regarding the PV generation forecasting and energy task scheduling modules tested for the premises of Institute Mihajlo Pupin.

Standard Full-Data classifiers in NLP demand thousands of labeled examples, which is impractical in data-limited domains. Fewshot methods offer an alternative, utilizing contrastive learning techniques that can be effective with as little as 20 examples per class. Similarly, Large Language Models (LLMs) like GPT-4 can perform effectively with just 1-5 examples per class. However, the performance-cost trade-offs of these methods remain underexplored, a critical concern for budget-limited organizations. Our work addresses this gap by studying the aforementioned approaches over the Banking77 financial intent detection dataset, including the evaluation of cutting-edge LLMs by OpenAI, Cohere, and Anthropic in a comprehensive set of few-shot scenarios. We complete the picture with two additional methods. First, a cost-effective querying method for LLMs based on retrieval-augmented generation (RAG), able to reduce operational costs multiple times compared to classic few-shot approaches, and second, a data augmentation method using GPT-4, able to improve performance in data-limited scenarios. Finally, to inspire future research, we provide a human expert’s curated subset of Banking77, along with extensive error analysis.