Optimization heuristics for residential energy load management

The MS thesis is concerned with the problem of scheduling the daily energy loads in a multihouse environment from the point of view of an energy retailer. We assume that the residential users own a set of home appliances (washing machines, dishwashers, ovens, microwave ovens, vacuum cleaners, boilers, fridges, water purifiers, irons, TVs, personal computers and lights) that are supposed to be used during the day. Houses can also be equipped with Photo Voltaic (PV) panels, which produce energy in a discontinuous way, and batteries that allow the system to store and release energy when required. The day is subdivided into 96 timeslots of 15 minutes each. For each appliance, we suppose to know the load profile, that is, a set of successive timeslots with the corresponding amount of energy required. Given the load profile of each appliance, the time windows in which the appliances must be executed, the physical characteristics of the batteries, the energy amount produced by the PV systems, the problem is that of scheduling the various appliances (assigning their starting timeslots) so as to minimize an appropriate objective function while respecting the maximum capacity of the meters (usually 3 kW). We consider minimizing the total maximum peak. This Residential Energy Load Management Problem is a challenging extension of the classical Generalized Assignment Problem (GAP). Since the Mixed Integer Linear Programming (MILP) formulation can be solved within reasonable computing time only for small instances, we developed various methods to tackle medium-to-large size instances: a Greedy Randomized Adaptive Search Procedure (GRASP) to generate initial feasible solutions, a meta-heuristic à la Tabu Search (TS) to improve initial solutions, and other techniques based on the solution of reduced MILP problems. In the TS algorithm we proposed different types of moves (appliances shift, batteries charge or discharge…) to explore the neighbourhood. We have tested our methods on a data set of 180 realistic instances with different number of houses (20, 200 and 400), PV panels and batteries. The solutions provided by the heuristics are compared with those obtained by solving the MILP model by using a state-of-the-art solver. For instances without batteries all our heuristics yield high quality solutions – within 3% from the reference solution – in a short computing time for the largest instances. Heuristics that solve reduced MILPs achieved the same results even for instances with batteries

Fault detection and diagnostics in ventilation units using linear regression virtual sensors

Buildings represent a significant portion of global energy consumption. Ventilation units are one of the largest components in buildings systems and are responsible for large part of energy consumption. Ventilation units are complex components, often customized for the specific building. Their faults impact buildings' energy efficiency and occupancy comfort. In order to ensure their correct operation, proper Fault Detection and Diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose to exploit physical relations inside the unit to create virtual sensors from other sensors' readings, introducing redundancy in the system. We create linear regression models for three sensors using other sensors related through physical laws as inputs. We use two different measures to detect when a virtual sensor deviates from the actual one: coefficient of determination and acceptable range. We test our method on a real building at the University of Southern Denmark. Our method detects a fault in temperature sensor, where its readings have an abnormal trend and fall outside acceptable ranae for one day.Postprint (author's final draft

A method for fault detection and diagnostics in ventilation units using virtual sensors

Buildings represent a significant portion of global energy consumption. Ventilation units are complex components, often customized for the specific building, responsible for a large part of energy consumption. Their faults impact buildings’ energy efficiency and occupancy comfort. In order to ensure their correct operation, proper fault detection and diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose exploiting physical relations inside ventilation units to create virtual sensors from other sensors’ readings, introducing redundancy in the system. We use two different measures to detect when a virtual sensor deviates from the physical one: coefficient of determination for linear models, and acceptable range. We tested our method on a real building at the University of Southern Denmark, developing three virtual sensors: temperature, airflow, and fan speed. We employed linear regression models, statistical models, and non-linear regression models. All models detected an anomalous strong oscillation in the temperature sensors. Readings fell outside the acceptable range and the coefficient of determination dropped. Our method showed promising results by introducing redundancy in the system, which can benefit several applications, such as fault detection and diagnostics and fault-tolerant control. Future work will be necessary to discover thresholds and set up automatic fault detection and diagnostics.Peer ReviewedPostprint (published version

Consensus-Based Method for Anomaly Detection in VAV Units

Buildings account for large part of global energy consumption. Besides energy consumed due to normal operation, a large amount of energy can be wasted due to faults in buildings subsystems. Fault detection and diagnostics techniques aim to identify faults and prevent energy waste, but are often difficult to apply in practice. Data-driven methods, in particular, require an adequate amount of fault-free training data, which is rarely available. In this paper, we propose a method for anomaly detection that exploits consensus among multiple identical components. Even if some of the components are faulty, their aggregate behaviour is overall correct, and it can be used to train a data-driven model. We test our method on variable-air-volume units in an existing building, executing two experiments grouping the components according to ventilation unit, and according to room type. The two experiments identified the same set of anomalous components, i.e., their behaviour was different from the rest of the group in both cases, and this suggests that the anomaly was not due to wrong group assignment. The proposed method shows the potential of exploiting consensus among multiple identical systems to detect anomalous ones

A method for fault detection and diagnostics in ventilation units using virtual sensors

Buildings represent a significant portion of global energy consumption. Ventilation units are complex components, often customized for the specific building, responsible for a large part of energy consumption. Their faults impact buildings’ energy efficiency and occupancy comfort. In order to ensure their correct operation, proper fault detection and diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose exploiting physical relations inside ventilation units to create virtual sensors from other sensors’ readings, introducing redundancy in the system. We use two different measures to detect when a virtual sensor deviates from the physical one: coefficient of determination for linear models, and acceptable range. We tested our method on a real building at the University of Southern Denmark, developing three virtual sensors: temperature, airflow, and fan speed. We employed linear regression models, statistical models, and non-linear regression models. All models detected an anomalous strong oscillation in the temperature sensors. Readings fell outside the acceptable range and the coefficient of determination dropped. Our method showed promising results by introducing redundancy in the system, which can benefit several applications, such as fault detection and diagnostics and fault-tolerant control. Future work will be necessary to discover thresholds and set up automatic fault detection and diagnostics.Peer Reviewe

Fault detection and diagnostics in ventilation units using linear regression virtual sensors

Buildings represent a significant portion of global energy consumption. Ventilation units are one of the largest components in buildings systems and are responsible for large part of energy consumption. Ventilation units are complex components, often customized for the specific building. Their faults impact buildings' energy efficiency and occupancy comfort. In order to ensure their correct operation, proper Fault Detection and Diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose to exploit physical relations inside the unit to create virtual sensors from other sensors' readings, introducing redundancy in the system. We create linear regression models for three sensors using other sensors related through physical laws as inputs. We use two different measures to detect when a virtual sensor deviates from the actual one: coefficient of determination and acceptable range. We test our method on a real building at the University of Southern Denmark. Our method detects a fault in temperature sensor, where its readings have an abnormal trend and fall outside acceptable ranae for one day

Imaging of metabolic bone disease

Osteoporosis is the most important metabolic bone disease, with a wide distribution among the elderly. It is characterized by low bone mass and micro architectural deterioration of bone tissue, leading to enhanced bone fragility and a consequent increase in fracture risk. Identify bone weakening with an appropriate and accurate use of diagnostic imaging is of critical importance in the diagnosis and follow-up of osteoporotic patients. The aim of this review is to evaluate the detection rates of the different imaging modalities in the evaluation of bone strength, in the assessment of fracture risk and in the management of fragility fractures

Imaging of metabolic bone disease

Osteoporosis is the most important metabolic bone disease, with a wide distribution among the elderly. It is characterized by low bone mass and micro architectural deterioration of bone tissue, leading to enhanced bone fragility and a consequent increase in fracture risk. Identify bone weakening with an appropriate and accurate use of diagnostic imaging is of critical importance in the diagnosis and follow-up of osteoporotic patients. The aim of this review is to evaluate the detection rates of the different imaging modalities in the evaluation of bone strength, in the assessment of fracture risk and in the management of fragility fractures