In this article, I review the research in the 2024 NeurIPS conference paper, From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection, by Wang et al. The paper explores integrating event analysis into LLM-based time-series forecasting models. Real-time text such as news articles or social media posts are underutilized in…
Photovoltaic (PV) systems are key to the transition toward clean energy, but their real-world performance is shaped by complex environmental interactions. For researchers working on the physics of solar energy systems, understanding how variables like temperature, humidity, aerosols, and wind impact panel efficiency provides a critical foundation for deeper modeling and analysis. This blog post…
Recent advancements in the field of Natural Language Processing (NLP) and Large Language Models (LLMs) have opened new avenues for processing qualitative data. This development is particularly interesting for power systems as they have an enormous amount of untapped qualitative data. Decision-making in the power system sector has relied on quantitative data. However, qualitative data…
This study explores the impact of weather features on short to medium electricity load prediction across diverse geographical locations. Using hourly load data, we evaluated the effectiveness of several feature selection methods, including Mutual Information (MI), Principal Component Analysis (PCA), Lasso, and Heatmap correlation. We benchmarked these feature selection methods with a hybrid deep learning model to investigate the impact of choosing…
Transformers, powered by scaling laws, have transformed the landscape of AI, but their performance in time series forecasting is less definitive. This post explores the effectiveness of transformers, the challenges of scaling, and the rise of specialized foundation models like TTM, MOMENT, and Chronos. Do these big models live up to the hype, or are…
In this paper, a new deep learning-based two-stage dataset-clustering/temporal-clustering method is proposed for time aggregation in renewable energy-integrated power systems. In this way, for the first time, the representative period, including one or more representative days, is obtained using a GAN-based model in which the LSTM Network is embedded in both generator and discriminator models.…
The U.S.-Canada Center on Climate-Resilient Western Interconnected Grid brings together leading experts in power engineering, climate, forestry, data analysis, policy and social sciences from a network of 35 partners. This new interdisciplinary center is aimed at fortifying the region’s power infrastructure against the floods, high winds, drought, even cold snaps that are also taking a…
The University of Calgary and the University of Utah will establish and co-lead the U.S.-Canada Center on Climate-Resilient Western Interconnected Grid through $5M funding by the U.S. National Science Foundation (NSF) and $3.75M funding by the Natural Sciences and Engineering Research Council of Canada (NSERC). Commonly known as “the Western Interconnection,” it is one of two…