Indoor lighting fault detection and diagnosis using a data fusion approach

Indoor lighting fault detection and diagnosis using a data fusion approachAn innovative and automated building diagnostics based on high-level correlation rules and data fusion, using severity indicators to diagnose and identify the possible causes

Francesco Marinofrancesco.ingmarino@gmail.com

In collaboration with

Introduction

The building sector is responsible for more than 40% of European energy consumption [1]. Poorly maintained and improperly controlled building equipment wastes an estimated 15% to 30% of energy*. To prevent this waste an appropriate ENERGY MANAGEMENT is needed, through widespread automated DIAGNOSTICS and CONTROL ACTIVITIES.

*EURIMA - European Insulation Manufacturers Association

IntroductionENEA Research Center’s Smart Village

In the ENEA research center the realization of a demonstrative prototype of Smart Village is under experimentation. Many smart activities are carried out, with an energy on demand approach: SMART BUILDING, SMART LIGHTING, SMART MOBILITY.

What is a Smart Building?

A Building Automation System (BAS) is an example of a distributed control system

The control system is a computerized, intelligent network of electronic devices designed to monitor and control the mechanical, electronics and lighting systems in a building

A building controlled by a BAS is often referred to as a SMART BUILDING

Focus on the F40 building

Located in the ENEA Research Center, it can be considered as an example of many old structures belonging to the public administration

3 floors

Constituted by offices and labs

Highly equipped with sensors and actuators

Smart Building

The Goal

Implementation of an innovative FDD (Fault Detection and Diagnostics) method based on data fusion for Smart Buildings

Innovative aspects

Applicable to buildings network

Energy and economic savings without making structural changes to the existing building but only acting on the management of its power consumption

Scale economy with the monitoring of the main working parameters of all the buildings

Methodology descriptionA first clear scheme [2]

Pre-processing indices of energy and environmental monitored data, for each of potential anomaly that can occur in a building, represents the symptoms. A situation assessment is done through the aggregation of the preprocessing previously specified.

Methodology descriptionPre-processing Situation assessment

S1a. Simultaneous lights switched on of an abnormal number of electrical utilities

S30. Large number of occupied rooms compared to working hours

P1a. Outlier electrical power

P1b. Outlier electrical energy

P8a. Abnormal electricity trend than the historical one (mon/tue – thur/fri/weekend)

P8b. Abnormal electricity trend than the previous week

P24c. Percentage of occupied rooms

P51. Working Schedule

The cause under exam is obtained combining the explained situations and also gives an important indication of the building energy performances and users’ behavior.

Methodology descriptionSituation assessment Cause

S1a. Simultaneous lights switched on of an abnormal number of electrical utilities

S30. Large number of occupied rooms compared to working hours

C13a. Indoor lights switched on in relation to occupied rooms and working hours

From sensors data, preprocessing indices is defined. P1a is fuzzified with a sigmoid expression to normalize the severity of the electrical peak detected, P51 with an inverse gaussian function.

Definition of the diagnostics rule

P1a P8aP1b P8b P51P24c

S1a S30

C13a

Fuzzy LogicFuzzy Logic

Sensors Data

Data FusionS1a = P1a OR P1b OR (0.7 * P8a + 0.3 * P8b)

Data FusionS30 = P51 AND P24c

Data FusionC13a = 0.7 * S1a + (1 - 0.7) * S30

Peak Detection

Calculate preprocessing

Situations assessment

Calculate cause

Is a fault detected?

Report fault details

Yes

The diagnostics process

No

The Peak Detection processSFunction definition

Local analysis window’s width

S is a peak function, which associates a score (a non-negative real number) S(i, xi, T) to each element of an uniformly sampled time-series T, containing N values. It computes the average of the maximum among the signed distances of xi from its k left neighbors and the maximum among the signed distances of xi from its k right neighbors [3].

S(k,i,xi,T) - m’ > h * s’

A 24 hours time span resulted the optimum between speed and reliability in detecting faults. The analysis takes in consideration 144 samples of power consumption per day (each one is acquired every 10 minutes). With a new sample, the chronologically oldest is eliminated.

The Peak Detection processSamples time series dimension

Diagnostics results

On 29/3/2013, a fault was manually generated to tune the parameters of the membership functions and to reveal effectively and in a reliable way the anomalous event.

A detailed notification of the detected faults was sent to the energy manager, where the values reveal their critical issues.

Diagnostics recap

Hour Number of faults Average value Max value

1 0 0 0

2 1 0.902 0.902

... ... ... ...

6 30 0.774 0.788

7 1 0.760 0.760

... ... ... ...

17 1 0.787 0.787

18 33 0.816 0.842

... ... ... ...

Faults for 2nd floor in detailCritical faultsSystematic faults due to cleaning services

Diagnostics recap

Floor Number of faults Average value Max value

0 102 0.716 0.857

1 57 0.764 0.852

2 72 0.799 0.902

Faults for each floor

Future directions● Keep going on collecting data in order to have a more accurate framework of

building’s energy situation providing eventually operations of maintenance.

● Study of new diagnostic rules applied to conditioning related to both electrical and thermal measurements. These aspects with the lighting one are the most important factors that affect energy consumption and continually require to be monitored.

● This diagnostics method will be applied to buildings network with the precise aim at monitoring more and diverse structures thinking in a future that it would be adopted in a large scale, in small districts or cities.

References1. IEA (International Energy Agency), Electricity/Heat Data for IEA Europe, 2008.

2. Khan, I., Capozzoli, A., Corgnati, S. & Cerquitelli, T., Fault Detection Analysis of Building Energy Consumption Using Data Mining Techniques, Energy Procedia 42, pp. 557 – 566, 2013.

3. Palshikar, G.K., Simple Algorithms for Peak Detection in Time-Series, Proc. of 1st Int. Conf. Advanced Data Analysis, Business Analytics and Intelligence, 2009.

Full paper available at:https://drive.google.com/open?id=0B9stuYwPgesya1NvZnlUNkIwYjQ