[HTML][HTML] Interpretable machine learning for building energy management: A state-of-the-art review
Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …
and flexibility in the past decade owing to the ever-increasing availability of massive building …
Exploring local explanation of practical industrial AI applications: A systematic literature review
In recent years, numerous explainable artificial intelligence (XAI) use cases have been
developed, to solve numerous real problems in industrial applications while maintaining the …
developed, to solve numerous real problems in industrial applications while maintaining the …
Survey on ontology-based explainable AI in manufacturing
Artificial intelligence (AI) has become an essential tool for manufacturers seeking to optimize
their production processes, reduce costs, and improve product quality. However, the …
their production processes, reduce costs, and improve product quality. However, the …
Investigating the impact of borehole field data's input parameters on the forecasting accuracy of multivariate hybrid deep learning models for heating and cooling
Heat pump systems coupled with Borehole heat exchanger (BHEx) are crucial for the
efficient and cost-effective heating and cooling of buildings. Accurate performance …
efficient and cost-effective heating and cooling of buildings. Accurate performance …
Data-driven insights for improved heating and cooling predictions: Impact of input parameters on multivariate deep learning algorithms using geothermal borehole …
The current research examines the effect of input parameters on the accuracy of deep
learning models for forecasting borehole heat exchanger (BHE) temperatures. The study …
learning models for forecasting borehole heat exchanger (BHE) temperatures. The study …
Automated and Systematic Digital Twins Testing for Industrial Processes
Digital twins (DT) of industrial processes have become increasingly important. They aim to
digitally represent the physical world to help evaluate, optimize, and predict physical …
digitally represent the physical world to help evaluate, optimize, and predict physical …
Addressing Explainability in Load Forecasting Using Time Series Machine Learning Models
M Bouzid, M Amayri - 2024 IEEE 12th International Conference …, 2024 - ieeexplore.ieee.org
Energy management is a crucial issue in the modern world, as it affects various aspects of
human life and the environment. It is a complex and challenging task that involves multiple …
human life and the environment. It is a complex and challenging task that involves multiple …
MACHINE LEARNING APPROACH FOR ENERGY CONSUMPTION ESTIMATION IN DISTRICT HEATING SYSTEMS
I Ćirić, M Tasić, M Ignjatović… - Innovative Mechanical …, 2024 - ime.masfak.ni.ac.rs
This paper explores the application of various machine learning methods for time series
estimation in district heating systems. The focus is on predicting heat load using supervised …
estimation in district heating systems. The focus is on predicting heat load using supervised …
Development of an XAI-Based Residential Load Forecasting Model
E Henriksen - 2022 - ntnuopen.ntnu.no
The ever-increasing complexity in the power system has introduced a higher demand for
forecasting to keep the grid stable. Load forecasting has been an integral part of planning …
forecasting to keep the grid stable. Load forecasting has been an integral part of planning …