Francesco Simmini

Bio: Francesco Simmini is an academic researcher from University of Padua. The author has contributed to research in topics: HVAC & Fault detection and isolation. The author has an hindex of 6, co-authored 13 publications receiving 230 citations.

Load forecasting for the efficient energy management of HVAC systems

Abstract: In this paper, Artificial Neural Networks (ANNs) are used to achieve cooling load forecasting in HVAC (Heating, Ventilating, and Air Conditioning) systems. Load forecasting is crucial in plant configurations making use of thermal storage technologies, where, during the nighttime, part or most of the energy required during daytime is produced at lower cost by cooling or icing water. Load forecasting is then needed to quantify the energy to be stored for the following daytime and to set up strategies for its release during daytime. Although many algorithms have been presented in the literature for load forecasting, they often need as input a large data set, that is not always available in practical situations. In this paper, we present an algorithm based on ANNs that allows to obtain sufficiently accurate load predictions by exploiting a limited data set, obtained by measuring quantities that are typically available in standard HVAC installations. Furthermore, knowledge of the current thermal load (which is needed to setup the data set for ANN training) can be obtained by using a load estimation algorithm previously proposed by some of the authors, that only need basic knowledge of the system hydronics. Another distinctive feature of the algorithm is the use of the AHU schedule as a means for inferring information on the internal loads, which is in general not available in practice. Simulation results for both CAV and VAV HVAC systems confirm the viability of the approach.

Model-based fault detection and diagnosis for centrifugal chillers

Abstract: Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller systems can lead to discomfort for the users, energy wastage, system unreliability and shorter equipment life. Faults need to be early diagnosed to prevent further deterioration of the system behaviour and energy losses. In this paper a model-based approach is used in order to detect important chiller systems faults. First, a linear dynamic black-box model is identified for each of the relevant characteristic features of the system during the normal functioning of the chiller. Then, an on-line correlogram method verifies the whiteness property of the residuals in order to distinguish anomalies from normal operations. A decision table, that matches the influence of anomalies with the characteristic features, allows to identify chiller faults. The proposed fault detection and diagnosis approach is assessed by using real chiller data provided by the ASHRAE research project RP-1043.

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Abstract: In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.

Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future

Abstract: Artificial intelligence has showed powerful capacity in detecting and diagnosing faults of building energy systems. This paper aims at making a comprehensive literature review of artificial intelligence-based fault detection and diagnosis (FDD) methods for building energy systems in the past twenty years from 1998 to 2018, summarizing the strengths and shortcomings of the existing artificial intelligence-based methods, and revealing the most important research tasks in the future. Challenges in developing FDD methods for building energy systems are discussed firstly. Then, a comprehensive literature review is made. All methods are classified into two categories, i.e. data driven-based and knowledge driven-based. The data driven-based methods are abundant, including the classification-based, unsupervised learning-based and regression-based. They showed powerful capacity in learning patterns from training data. But, they need a large amount of training data, and have problems in reliability and robustness. The knowledge driven-based methods show powerful capacity in simulating the diagnostic thinking of experts. But, they rely on expert knowledge heavily. It is concluded that new artificial intelligence-based methodologies are needed to be able to combine the advantages of both kinds of methods in the future.

Anomaly Detection for IoT Time-Series Data: A Survey

Abstract: Anomaly detection is a problem with applications for a wide variety of domains; it involves the identification of novel or unexpected observations or sequences within the data being captured. The majority of current anomaly detection methods are highly specific to the individual use case, requiring expert knowledge of the method as well as the situation to which it is being applied. The Internet of Things (IoT) as a rapidly expanding field offers many opportunities for this type of data analysis to be implemented, however, due to the nature of the IoT, this may be difficult. This review provides a background on the challenges which may be encountered when applying anomaly detection techniques to IoT data, with examples of applications for the IoT anomaly detection taken from the literature. We discuss a range of approaches that have been developed across a variety of domains, not limited to IoT due to the relative novelty of this application. Finally, we summarize the current challenges being faced in the anomaly detection domain with a view to identifying potential research opportunities for the future.