Energy Disaggregation of Industrial Machinery Utilizing Artificial Neural Networks for Non-intrusive Load Monitoring

This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.

Explainability and Interpretability in Electric Load Forecasting Using Machine Learning Techniques–A Review

Electric Load Forecasting(ELF)is the central instrument for planning and controlling demand response programs,electricity trading,and consumption optimization.Due to the increasing automation of these processes,meaningful and transparent forecasts become more and more important.Still,at the same time,the complexity of the used machine learning models and architectures increases.Because there is an increasing interest in interpretable and explainable load forecasting methods,this work conducts a literature review to present already applied approaches regarding explainability and interpretability for load forecasts using Machine Learning.Based on extensive literature research covering eight publication portals,recurring modeling approaches,trends,and modeling techniques are identified and clustered by properties to achieve more interpretable and explainable load forecasts.The results on interpretability show an increase in the use of probabilistic models,methods for time series decomposition and the use of fuzzy logic in addition to classically interpretable models.Dominant explainable approaches are Feature Importance and Attention mechanisms.The discussion shows that a lot of knowledge from the related field of time series forecasting still needs to be adapted to the problems in ELF.Compared to other applications of explainable and interpretable methods such as clustering,there are currently relatively few research results,but with an increasing trend.

Typical load profile-supported convolutional neural network for short-term load forecasting in the industrial sector

This paper investigates how existing forecasting models can be enhanced to accurately forecast the electric load at factory level,enabling industrial companies to shift consumption to times of low energy costs.The model architecture must outperform state-of-the-art models and be sufficiently robust for use in multiple factories with low effort for specific applications.Moreover,this work focuses on the processing of high-resolution input data available almost in real time from multiple submeters after the main meters.The theory of load forecasting and related works are summarized in a first step including the requirements of forecasting models applied at factory level.Based on existing models,a new hybrid machine-learning model is proposed,combining a decision tree-based typical load profiler with a convolutional neural network that extracts features from multidimensional endogenous inputs with measurements of the preceding two weeks for multi-step-ahead load forecasts updated almost in real time.Furthermore,a multi-model approach is presented for calculating bottom-up forecasts with submeter data aggregated to a main-meter forecast.In a case study,the forecasting accuracy of the hybrid model is compared to both base models and a seasonal naïve model calculating the load forecasts for three factories.The results indicate that the proposed typical-load-profile-supported convolutional neural network for all three factories achieves the lowest forecasting error.Furthermore,it is validated that a reduction in data transfer delay leads to better forecasts,as the forecasting accuracy is higher with near real time data than with a data transfer delay of one day.Thus,a model architecture is proposed for robust forecasting in digitalized factories.