Supporting energy efficiency optimization in lighting design process

Supporting energy efficiency optimization in lighting design process Leszek Kotulski1, Jeroen De Landtsheer2, Sven Penninck2, Adam Sędziwy1, Igor Wojnicki1 1 Department of Applied Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland, 2 EANDIS, Gebouw Waterkant, Brusselsesteenweg 199, 9090 Melle, Belgium {kotulski,sedziwy,wojnicki}@agh.edu.pl, {jeroen.delandtsheer,sven.penninck}@eandis.be Abstract. In the article we discuss the properties of a designer-friendly solution finder based on accurate photometric computations. Such software automates reconfigurations and recalculations of a scene implied by changes introduced to a project by designer, keeping a balance between visualization features and availability of quantitative photometric data. The goal of the redesign in achieving power usage reduction. In the article we also deal with the practical approach to the problem presented by the Eandis, the Belgian distribution system operator. 1. Introduction The existing software market supplies a range of tools supporting the lighting design process. Basically it offers photometric calculations with limited design capabilities. In particular, most of them are limited to street-like configurations and none allow for design optimization toward energy efficient solutions. The new developed software will allow optimizing lighting on different public areas (streets, sidewalks, squares, parks) and will take into account smart control including dimming, presence detection and so on . In the article we discuss the issue of filling this gap by preparing a designer-friendly solution finder, based on accurate photometric computations. Such software automates reconfigurations and recalculations of a scene implied by changes introduced to a project by designer, keeping a balance between visualization features and availability of quantitative photometric data. The software is tested with practical cases provided by a grid operator, to determine energy savings for cities and communities. This project is the effect of collaboration between AGH University of Science of Technology in Cracow and Eandis (the Belgian grid operator for electricity and gas). AGH University of Science and Technology developed software and Eandis provided AGH UST with test cases and did testing on the developed software. The article is organized as follows: Section 2 contains the overview of lighting design process paradigm and in Section 3 we consider the workflow in a typical lighting design task. Optimization of the street lighting energy efficiency and its computational complexity are presented in Section 4. The impact of the control for energy efficiency is discussed in Section 5. In Section 6 photometric calculation software, PhoCa, created in AGH UST, is presented. Section 7 focuses on the practical approach to street lighting 1 optimization on the example of Eandis grid operator practice. The summary and the directions for future works are included in Section 8. 2. Outdoor Lighting Design Paradigm Outdoor lighting design process may be considered from several perspectives, dependently on a role played by its participants [4]. Objectives of an architect are often not fully compliant with a street light electrician ones, and require the cooperation of both parties. Yet another perspective may be represented by an investor or a customer. This multiplicity of approaches makes the design be a complex and iterative process with multiple interactions among particular actors. Obviously, this enlarges a design time. The paradigm of the outdoor lighting design relies on three basic elements described below: Aesthetics – this issue plays a special role in architectural lighting, e.g., in highlighting buildings, monuments, pavements, squares and so on. Objectives related to the aesthetics have to be agreed between an architect and an investor. Sometimes they are constrained by some local regulations, obligatory for new investments. Functionality includes both the compliance with existing lighting standards (e.g., EN 13201:2) and the support for social goals such as public safety [3], improved orientation, highlighting identity of places and others [1]. Cost-effectiveness is the only fully measurable criterion of the lighting design assessment. The cost-effectiveness is a resultant of several factors: 1. Investment costs 2. Maintenance costs 3. Energy prices 4. Fixture energy efficiency Let us note that cost-effectiveness may be expressed in various ways, for example in terms or investment payback period, net present value (NPV) or, in the most intuitive case, in terms of the energy usage reduction. It should be strongly emphasized, however, that fulfilling the last postulate does not guarantee optimizing a cost-effectiveness, which depends on other parameters, as it was mentioned above. Moreover it is possible that for a given power level we have several alternative solutions [2]. One may distinguish three basic types of actors involved in various phases of a design process (Fig.1). Functionality of lighting design software has to satisfy their needs. Figure 1. Three actors of the design process. 2 Designer perspective The first actor is a designer. The required software capability as seen from this perspective is suggesting an optimal configuration of a lighting solution to be prepared. It should be remarked that the understanding of an optimality notion varies dependently on primary objectives accepted for a given design. For example, when we consider a retrofit of street lighting and if the power usage reduction is an only issue then software should suggest fixture models (selected from a defined pool of fixtures) which imply the minimum energy consumption and ensure the accordance with corresponding lighting standards. Investor perspective An investor is the second actor of a lighting design process. As an entity capable of investment decisions making he needs to possess all information necessary to do it. In this context, software is expected to provide a comprehensive and detailed information about the structure of potential gains obtained thanks to selecting a given re-design scenario (e.g., which areas of a lighting solution yield the highest power reduction). Also suggesting other, alternative scenarios accompanied with corresponding pros and cons, is very helpful. The additional software function, necessary for decision making is generating detailed quantitative reports describing how a new design compares to an existing one. Customer perspective The third actor, which is referred to as a customer, may be partially identified with investor. There doesn’t exist, however, a straightforward relation between both roles. The software capability considered for this actor is (apart from finding the solution being energy-optimal, payback-optimal and so on) reduction of the design life cycle time. To accomplish that a program should provide multi-scenario output. Thus several iterations of a project development, made between a customer and a designer, may be reduced to a single one. Regarding the single solution design time, this reduces significantly overall design time. 3. Design Process The lighting design process (see Figure 2) is started by a lighting designer or architect. Spatial and compositional assumptions regarding the architectural space are made resulting in conceptual sketch. The design at this stage is very informal, sketching software is being used (e.g. Google SketchUp). The sketch represents a general view of the scene with light points indicated and their general parameters in terms of light cones. It is manly to identify where the light points should be and where the actual light should go. Figure 2. Design process. 3 Then the sketch is transformed into a two or three dimensional (2D, 3D) technical drawing resulting in a spatial concept, a wire-frame. This allows to precisely specify places which are to be illuminated, color, temperature, quality and other parameters of a lighting composition. The drawing is performed by a supporting software such as: AutoCad, ArchiCad, Revit etc. A next step, verification, is performed by a lighting engineer. Luminaires and intensities of light sources are selected, according to the assumptions provided by the designer. Furthermore, they are verified using photometric software (Dialux, Calculus, Ulysse or similar) if technical capabilities of luminaries meet requirements of the project. This phase impacts number, power and detailed specifications of the luminaires. Since the technical drawing (wire-frame) can contain multiple elements not influencing photometrics, it has to be tuned accordingly or even created from very beginning. Next, the parameters calculated in the previous stage are given to the lighting designer to verify. It can be achieved through preparing a three dimensional, photorealistic visualization, a 3D model. It is supported by yet another software (e.g. 3ds Max, Maya). A final effect is analyzed. If it does not satisfy the designer it is adjusted accordingly and the process loops back to the spatial concept (wire-frame) or verification (Photometric calculations) stages. Based on the adjustments of the 3D model the results from previous steps need to be updated. These steps are performed iteratively until satisfying results are achieved, being a trial and error process. Since each stage is isolated some errors or artifacts can be introduced unwillingly in the process. This leads to inconsistencies and lengthens the entire process. It needs to be pointed out that there is lack of automation between subsequent stages. Data produced as a result of the conceptual stage need to be interpreted and recreated as a wire-frame model and so on. Human interactions are required for transiting data between subsequent stages. The most problems are caused by taking the corrections and feeding them back to the verification and adjustments (photometrics) stage. Multiple iterations, to achieve a satisfying result, might cause even more mistakes and elongate the entire process. In general entire process can be optimized by: reducing number of human oriented interactions and increasing effectiveness of selecting and applying parameters to light points (see Figure 3). Reducing number of human-oriented interactions is to unify data interfaces among applications to ensure proper import and export and their automation. Data flow among applications should be provided with minimal human interactions. Calculation and application light point parameters to the design, including corrections, contribute significantly to the effectiveness of the entire process. In this stage actual optimization criteria are applied which may vary depending on requirements. Theses are: energy consumption reduction, public safety increase, overexposure elimination etc. From this perspective the design is perceived as yet another set of constraints in addition to the ones mentioned above. 4 Figure 3. Desired design process. The main focus regards the loop, which is transitions: 3, 4 and 5 (Figure 3). It covers photometric calculations, optional 3D visualization, and applying corrections to the design, which require recalculations in turn. The key element is to improve quality, precision and usability of photometric calculations which have been identified as the bottleneck. A prototype software providing the calculations and optimizing light points parameters have been implemented. Automation of transitions is indicated accordingly (compare with Figure 2). 4. Energy Efficiency in Design Process. Computational Challenges There are several common known, methods of reducing a lighting system energy usage such as: (i) installing low wattage luminaries, e.g. LEDs, (ii) adjusting a system performance to the actual state of an environment, (iii) reducing a road class (in terms of EN 13201:2 requirements) for the night time, and adjusting the performance characteristics accordingly, (iv) implementing the proper policy of the non-recoverable light loss compensation. Their applicability varies dependently on an actual design background (new installation, retrofit, fixture types to be used, financial constraints and so forth). The decline in LEDs prices together with their continuous technological development make LED to be the most promising technology regarding the approaches listed above. On the other side, however, multiple LED-based street lighting installations require using proper software tools oriented for obtaining maximum benefits of such advanced properties. Those properties (stepless dimming, extremely short onset times or possibility of preparing user-defined photometric solids) enable preparing solutions profiled strictly for a particular customer. The closer look at that reveals the practical problem which influences a design task. This problem is related to the complexity of finding a design solution meeting obligatory photometric requirements. To illustrate above let us consider the following scenario of designing a new street lighting for a road of a given class, say ME4a (according to DIN EN 13201:2, see Table 1). It is assumed that parameters of an installation, listed in the Table 2 may be varied in a process of finding an optimal solution. The table presents assumed variability ranges, corresponding step sizes and resultant number of variants for each parameter. The total number of variants, N, obtained from the Table 2 is 5 N=41 x 21 x 3000 x 21 x 41 x 51 = 1,13 x 1011. Even if applying some heuristics based on expert knowledge/experience or good practices (e.g., assuming that the optimum distance between two neighboring poles is determined by the position of the ground point where the maximum candlepower outputs meet) the search space still remains too big to find a solution in a reasonable time, in particular with no computer support. Table 1. ME lighting classes according to EN 13201:2 Class Lw [cd/m2] Uo UI TI % SR ME1 2,0 0,4 0,7 10 0,5 ME2 1,5 0,4 0,7 10 0,5 ME3a 1,0 0,4 0,7 15 0,5 ME3b 1,0 0,4 0,6 15 0,5 ME3c 1,0 0,4 0,5 15 0,5 ME4a 0,75 0,4 0,6 15 0,5 ME4b 0,75 0,4 0,5 15 0,5 ME5 0,5 0,35 0,4 15 0,5 ME6 0,3 0,35 0,4 15 n/a Due to the combinatorial explosion implied by the high number of degrees of freedom in optimization tasks, the new approach to the photometric computations has to be applied. As the brute force method (i.e., testing all potential solutions contained in a search space) fails for problems similar (or more complex) to the above one, more sophisticated methods based on artificial intelligence computational techniques are applicable here. 6 Table 2. The exemplary list of variables for an optimization process Parameter name Start value End value Step Number of variants Lamp spacing [m] 20,0 40,0 0,5 41 Pole height [m] 8,0 12,0 0,2 21 Fixture type n/a n/a n/a 30001 0 20 1 21 Overhang [m] -2,0 2,0 0,1 41 Dimming [%] 0 50 1 51 Tilt [deg] 5. (Re)Design and Control Economic profits (we focus on the power usage only, neglecting other factors like a payback period) implied by using an advanced lighting technology, e.g, LED, may be obtained twofold. Firstly, the luminous efficacy and lifetime of a light source (see Table 3) bring power savings through the replacement only (retrofit). According to manufacturers’ data2, energy usage reduction can reach the level up to 40-45%. The mentioned properties of LED lamps (such as stepless dimming or low onset time), however, create possibilities of further savings achieved by implementing suitable control schemes. The concept of a responsive lighting control system relies on cooperation of three elements: lighting infrastructure (luminaries, power cabinets, power lines), telemetry layer (including various types of sensors) and control center, coupling the first and the second. Design of street lighting installations is usually considered separately from a lighting control system. For lighting systems covering larger areas it is advisable to merge both perspectives. In such a case lighting design gets augmented by adding the telemetry layer but also by affecting objectives which have to reflect the overall system performance. 1 The number of 3000 is a rough approximation of the number of outdoor fixture models/types produced by three leading manufacturers, assuming that each of them offers at least 1000 models/types. (The last assumption seems to be reasonable: General Electric LED fixture, Cobrahead, offers up to 650 photometric solids). Obviosuly, some types will be a priori rejected as not compatibile with a technical specification. 2 E.g., those supplied by GE Lighting. 7 Table 3. Light source characteristics3 Onset time† Light Source Efficacy (lm/W) Life (hours)* Tungsten filament lamp 12-20 750-4000 Fluorescent (incl. compact) 60-100 10,000-30,000 1-60 s Metal halide 80-110 10,000-20,000 60-300 s Xenon 30-60 1000-5000 1 μs Light-emitting diode (white) 90-130 50,000-100,000 10-20 ns 0.1-0.3 s *Assuming steady operation. †Time to reach 90% of maximum light output. 6. PhoCa Software Within the international R&D project, Products and Services of a Living Smart Energy City Lab, carried by AGH University of Science and Technology, EANDIS and other partners, the prototype software, PhoCa (abbrev. of Photometric Calculations) is developed, for optimizing street lighting installations (Figure 4). Figure 4. PhoCa screenshot Besides typical functionality of photometric software like Dialux, Ulysse, Calculux Road and so on, program offers an optimization performed with respect to the userdefined criteria. Moreover, new algorithms reducing the computation time are applied. Tests conducted with real data provided by EANDIS, showed that tuning of working parameters of existing street lighting installations based on high pressure discharge lamps, brings power usage reduction of the order 5-8%. 3 Source: John D. Bullough, Yiting Zhu, Nadarajah Narendran,Characteristics of Light-Emitting Diode Sources: Relevance for Visual Signal Detection, Lighting Research Center, Rensselaer Polytechnic Institute, 21 Union Street, Troy, NY 12180, USA, (2012) 8 Figure 5. 3D visualization of photometric calculations with Maya 7. Improving Energy Efficiency – Practical Approach Public lighting consumes a lot of energy. The energy cost of public lighting takes in general 40% to 50% of the city total energy budget . As it was mentioned earlier, new energy savings (and reductions in energy costs) can be realized with investments in energyefficient public lighting installation. Thus the actual problem is: how to find the energy efficiency potential on existing and new public lighting installations ? Following input is needed to answer this question: The required lighting class for each road (ME, CE, … classes; according to EN 13201:2). (ii) The detailed road configuration (road width, spacing, position of the luminary according to the road, actual fixture, actual type of lamp, actual power, mounting heights, etc.). (iii) Lighting calculations to compare the actual lighting installation with new optimized (energy-efficient) one(s). Today distribution system operators (abbrev. DSOs) are bumping into several issues when getting this input. They will be highlighted below. (i) Different lighting classes for same type of roads-streets EN 13201 standard offers the method of finding a lighting class for each road type. This is a subjective method, depending on the interpretation of an expert and for that reason this gives different results and even requires a measurement of the traffic density for the road in scope. An easier way to define the required lighting class for a road is to classify roads into different ‘zones’ (ex.: city center, shopping area, residential, industrial, parking and so on). Each ‘zone’ may be assigned with a corresponding lighting class. To find these assignments, Eandis supports the cities/communities with a ‘master plan public lighting’. 9 This master plan results in a clear view on a city’s requirements for the public lighting. Each a road in scope is then classified to a zone and each zone is linked to the required lighting level (lighting class). DSOs do not track the full road configuration Most asset managers of a public lighting installation (e.g., Eandis) track data of luminaries, type of lamps, power, mounting heights and others but in general they do not record data related to the geometry of luminaries arrangement like road width, position of a luminary wrt the road, fixture inclination, lamp spacing and similar. To collect these data, Eandis started using Mobile Mapping technology. The mobile mapping gives us the missing inputs for the required lighting calculations. High performance lighting calculation software is the key for optimization When performing lighting calculations, software is needed to calculate the lighting result from the different inputs. Most lighting calculation software on the market give DSOs the possibility to check a result of a single configuration per an executed calculation task. Some software have a sort of solution wizard, making calculations between fixed limits (e.g., PhoCa software mentioned above), but actually we are still searching, as the DSO, for the lighting calculation software that works with an input of the mobile mapping (i.e., an extended lighting infrastructure database). Today DSO Eandis needs to execute a mass of separate calculation tasks using standard lighting calculation software to find the optimizations of public lighting installations. There is a huge demand for lighting calculation software which quickly compare different configurations in a flexible manner (some limits fixed, e.g., spacing, mounting height), others not (e.g., luminary, type of lamp, power). 8. Conclusions And Future Work The high performance software (in terms of low computation times) for photometric computations is the highly demanded tool supporting the redesign of public lighting towards power consumption reduction. The prototype software, PhoCa, was created in AGH University of Science and Technology. The test showed that it allows for obtaing 5-8% reduction of the power usage. Further development of the software focuses on implementing automation and visualization in the process described in Section 3. As a proof of concept a prototype tool has been implemented for 3D visualization. It is an extension to Maya rendering and animation software. It mainly improves transition 3 (see Figure 3) by integrating photometric calculations with the rendering engine. It is showed in action in Figure 5. The scene consists of a flat urban area with four lamp posts. At each lamp post there is a luminary (a light point) with given parameters. While the rendering engine shows how the scene would look like photo-realistically, the photometric engine indicates underexposed and overexposed regions (underexposure at the outer rim). Furthermore, the proposed extension will be capable of calculating and optimizing luminary parameters, minimizing or maximizing given criteria function e.g. power consumption, public safety, overexposure etc. It can also optimize number of light points or their distribution, proposing corrections to the design. The proposed extension is highly interactive. While changing light point parameters, the over and under-exposure is 10 interactively calculated and visualized in real time. There will be no need to switch back and forth between photometric calculation tool and 3D visualization one anymore. References [1] A. Sędziwy, M. Kozień-Woźniak, Computational Support For Optimizing Street Lighting Design, Complex Systems and Dependability, vol.170, Advances in Intelligent and Soft Computing, Ed. W. Zamojski et al., Springer Berlin Heidelberg, 2012 [2] Boyce, P., Hunter, C., Vasconez, S., An Evaluation of Three Types of Gas Station Canopy Lighting. Technical Report. Lighting Research Center, Rensselaer Polytechnic Institute, Troy, NY 12180-3352, 2001 [3] IESNA Lighting Handbook, Illuminating Engineering Society of North America (Author), Mark Stanley Rea (Editor), 9th Edition, 2000 [4] Duco Schreuder, Outdoor Lighting: Physics, Vision and Perception, Springer Science + Business Media B.V., 2008 11