首页|Findings from Latrobe University Broaden Understanding of Artificial Intelligen ce (An artificial intelligence framework for explainable drift detection in ener gy forecasting)
Findings from Latrobe University Broaden Understanding of Artificial Intelligen ce (An artificial intelligence framework for explainable drift detection in ener gy forecasting)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting originating from Latrobe Universi ty by NewsRx correspondents, research stated, "Accurate energy consumption forec asting is crucial for reducing operational costs, achieving net-zero carbon emis sions, and ensuring sustainable buildings and cities of the future." Our news journalists obtained a quote from the research from Latrobe University : "Despite the frequent use of Artificial Intelligence (AI) algorithms for learn ing energy consumption patterns and predictions in Building Science, relying sol ely on these techniques for energy demand prediction addresses only a fraction o f the challenge. A drift in energy usage can lead to inaccuracies in these AI mo dels and subsequently to poor decision-making and interventions. While drift det ection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discus sed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, a imed at addressing these challenges. The proposed framework is composed of energ y embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainabi lity capability. The framework is empirically evaluated in the real-world settin g of a multi-campus, mixed-use tertiary education setting in Victoria, Australia ."
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