Load Forecasting of Food Retail Buildings with Deep Learning

Carolyn Goodman, J. Thornburg, S. Ramaswami, J. Mohammadi
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引用次数: 2

Abstract

Electrical grids are traditionally operated as multi-entity systems with each entity managing a geographical region. The current movement toward energy democratization and decarbonization is resulting in higher penetration of distributed energy resources (DERs) and intermittent, renewable generation. This process in turn is increasing the number of grid entities (agents). The paradigm shift is also fueled by increased adoption of intelligent sensors collecting data and actuators for advanced processing and computing. Predicting the future load of different consumers has become increasingly important for grids as they must balance intermittent generation to meet instantaneous demand. The main challenges in demand forecasting stem from the heterogeneity of loads and their data. Deep learning provides tools to utilize the collected data for predicting future load profiles and anticipating high-demand scenarios. This article presents a deep learning approach for load forecasting of commercial buildings with multiple refrigeration units. It then presents a case study demonstrating the efficacy of this approach for predicting refrigeration and freezer load in food retail stores.
基于深度学习的食品零售建筑负荷预测
电网传统上是作为多实体系统运行的,每个实体管理一个地理区域。当前能源民主化和脱碳运动正在导致分布式能源(DERs)和间歇性可再生发电的更高渗透率。这个过程反过来又增加了网格实体(代理)的数量。越来越多地采用智能传感器收集数据和执行器进行高级处理和计算,也推动了范式转变。预测不同用户的未来负荷对电网来说变得越来越重要,因为他们必须平衡间歇性发电以满足瞬时需求。需求预测的主要挑战来自于负荷及其数据的异质性。深度学习提供了工具来利用收集的数据来预测未来的负载概况和预测高需求场景。本文提出了一种用于多制冷机组商业建筑负荷预测的深度学习方法。然后,它提出了一个案例研究,证明这种方法的有效性,以预测冷藏和冷冻负荷在食品零售商店。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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