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Author: Hamidreza Zareipour Although all research presentations share a common goal of conveying knowledge, the most effective ones are tailored to the specific context and audience. These factors can drastically shape both the content and style of your talk. Presenting your findings at a final thesis defence differs in essential ways from delivering a presentation…
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…
This article introduces emissions response, the widespread use of real-time emissions factors in electricity grids as a signal for a dynamic response to facilitate decarbonization and system efficiency. Many publications have suggested dynamic approaches to addressing systemwide emissions, reducing emissions through time-sensitive consumption and aiding in the adoption of renewable and low-emissions technologies. With emissions…
Degradation of energy capacity and energy efficiency of lithium-ion battery energy storage systems depend on their operation conditions and are key factors in their business case feasibility. Using traditional energy reservoir models with energy throughput methods to quantify degradation leads to less accurate revenue estimates and unfeasible dispatch scenarios. This paper proposes a new neural…
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.…
Our lab is led by Dr. Hamidreza (Hamid) Zareipour, an internationally renowned researcher in power grid operation and planning and a highly respected figure in the Alberta’s electricity ecosystem and beyond.
Our team is driven by curiosity and a commitment to transformative research for electricity sustainability, affordability and resilience.
Contact us to discuss how we can help you achieve your energy transformation goals.
