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Open access Sep 2026

Effects of Operational-State Features on One-Week-Ahead Building Electricity Demand Forecasting Using a Temporal Fusion Transformer

Accurate one-week-ahead building electricity demand forecasting is essential for building energy management, yet representing future building operational characteristics remains challenging because such information is generally unavailable in advance. This study investigates the effectiveness of representing building o...

Hitoshi Naruse, Yuhi Baba, M. Yamaha · 0 citations
Conference Jul 2026

Multi-Source Building Energy Load Forecasting using a Transformer-Mamba Framework for Sustainable Infrastructure

Accurate forecasting of multi-source building energy loads is an important requirement in modern civil engineering for efficient building operation, infrastructure resilience, and sustainable resource utilization. Conventional forecasting approaches often experience limitations in representing nonlinear demand variatio...

Paulson Geo Philip · 0 citations
Open access Aug 2026

Data-Driven Modeling of Auxiliary Consumption in Utility-Scale BESS

Accurately modeling the auxiliary power consumption of Battery Energy Storage Systems (BESSs) is increasingly important as grid-scale storage assets are becoming involved in electricity markets. In this paper, we develop a data-driven framework to characterize and forecast auxiliary consumption using operational data f...

Aleksandar Dimovski, Matteo Spiller, Mershad Pakjoo et al. · 0 citations
Open access Aug 2026

Day-Ahead Cooling Load Forecasting for District Cooling System Based on Baseline-Morphology Decomposition

Against the backdrop of global climate change and energy structure transition, district energy systems have garnered significant attention for their efficiency and sustainability. Accurate load forecasting is crucial for enhancing the operational efficiency of district cooling systems. However, as typical dynamic time-...

Yue Liu, Hua-Biao Kong, Yakai Lu et al. · 0 citations
Open access Aug 2026

One-Hour-Ahead Short-Term Electricity Load Forecasting Using Long Short-Term Memory Networks

Accurate one-hour-ahead electricity load forecasting sustains dispatch, reserve planning, and dependable power-system operation, but additional inputs do not always improve predictions. This study assesses how different parameter settings contribute to power demand forecasting. Four long short-term models with a unifie...

Jingkai Gao · 0 citations
Open access Jul 2026

Residential photovoltaic generation forecast via long short-term memory and transformer

A systematic comparison of Long Short-Term Memory and transformer-based architectures for deterministic short-term PV power forecasting using publicly accessible data from multiple climatic regions highlights the advantages of attention-based sequence modeling for PV applications and offers practical guidance on featur...

Marcel Lüdecke, Elias Oppermann, Michel Meinert et al. · 0 citations

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