An artificial intelligence framework for explainable drift detection in energy forecasting

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

Abstract

Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms for learning energy consumption patterns and predictions in Building Science, relying solely on these techniques for energy demand prediction addresses only a fraction of the challenge. A drift in energy usage can lead to inaccuracies in these AI models and subsequently to poor decision-making and interventions. While drift detection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discussed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, aimed at addressing these challenges. The proposed framework is composed of energy embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainability capability. The framework is empirically evaluated in the real-world setting of a multi-campus, mixed-use tertiary education setting in Victoria, Australia. The results of these experiments highlight its capabilities in detecting concept drift, adapting forecast predictions, and providing an interpretation of the changes using energy embeddings.

Abstract Image

能源预测中可解释漂移检测的人工智能框架
准确的能耗预测对于降低运营成本、实现净零碳排放以及确保未来建筑和城市的可持续发展至关重要。尽管建筑科学领域经常使用人工智能(AI)算法来学习能耗模式和进行预测,但仅靠这些技术来预测能源需求只能解决挑战的一小部分。能源使用的偏移会导致这些人工智能模型的不准确性,进而导致决策和干预的失误。虽然已有漂移检测技术的报道,但现有文献中还没有讨论过一种可靠、稳健的方法,能够以可操作的见解解释已识别的差异。因此,本文提出了一种可解释漂移检测的能耗预测人工智能框架,旨在应对这些挑战。所提出的框架由能源嵌入、集成在数据仓库中的优化维度模型和可扩展的云实施组成,用于有效检测具有可解释性的漂移。该框架在澳大利亚维多利亚州一个多校区、混合使用的高等教育环境中进行了实证评估。实验结果凸显了该框架在检测概念漂移、调整预测预报以及利用能量嵌入对变化进行解释方面的能力。
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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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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