Elsevier

Energy and Buildings

Volume 138, 1 March 2017, Pages 655-665
Energy and Buildings

Integrative algorithm to optimize skylights considering fully impacts of daylight on energy

https://doi.org/10.1016/j.enbuild.2016.12.045Get rights and content

Abstract

In this paper we propose an algorithm to find optimal design of skylight for a one-storey office building while saving source energy consumption. The algorithm simultaneously couples daylighting with energy performance and compares different ratios of skylight to floor area based on both lighting and HVAC loads. Our method integrates different simulation tools with numeric optimization and exhaustive search to find the optimal ratio. More specifically we implemented EnergyPlus as a thermal engine and Radiance as a daylight engine, both of which are embedded in Ladybug and Honeybee. As Ladybug and Honeybee is a plugin for Grasshopper, we developed the algorithm in Grasshopper environment. We used Python and Grasshopper to integrate different simulation tools and implemented two methods of numeric optimization (gradient descent) and exhaustive search (parametric analysis) in order to validate the final result. After applying the proposed algorithm for a small office building in San Francisco, both methods coincide that the optimal skylight to floor ratio is 5.5–6% while decreasing the energy demand by 19%. The experiments show that the energy efficiency only occurs for skylight ratios of 3–14%. In addition, we used sDA and UDI metrics to analyze daylighting performance of each parametric scenario. We infer that only a specific range of skylight ratios that are energy efficient (5–10%) provide adequate daylight and avoid glare. The integrative process and optimization method proposed in this paper enable designers to find a robust energy efficient skylight design.

Introduction

Nowadays building industry has entered an era with a view toward minimizing an environmental footprint; however, toplights as energy efficient alternatives are often ignored in this industry. Statistics show that despite the potential of daylighting only approximately 2–5% of commercial building floor space currently has sufficient skylight area [1]. Lawrence and Roth discussed that there have been some unavoidable cautions such as roof leakage, direct solar radiation, heat gain and heat lost that make designers reluctant to apply toplights [1]. While today's technological advancement can mitigate the risk of the roof leakage, it is necessary to train practitioners how to install toplights and educate them how toplights as daylighting strategies impact electrical lighting loads and overall energy consumption of a building. To assess the overall energy consumption including heating, cooling and lighting, an integrative approach is needed. There has been an increasing effort to improve tools’ capabilities by coupling them to examine the impact of daylight on electrical lighting loads as well as heating and cooling loads [2], [3], [4]. In addition to the necessity of integration between different tools, the exhaustive process of parametric analysis makes it challenging to find robust design solutions. To boost the application of toplights, we facilitate the process of integration and parametric analysis by developing an algorithm to optimize Skylight Floor area Ratio (SFR) for a one-storey office building while enhancing total energy performance.

Despite the stagnant practice, implementation of toplights improves the quality of life and the environment. A body of literature has been dedicated to show daylighting boosts quality of life via positive impacts on health, well-being and moods, as well as reduction of fatigue [1], [5], [6]. A separate area of knowledge investigates the quantitative side of toplighting that includes its ability to replace electrical lighting and decrease energy consumption as well as CO2 emissions. These findings are significant because electrical lighting loads account for 20.5% of source energy for commercial buildings while the commercial building sector contributes to 19% of total energy consumption in the U.S. [7]. Preliminary studies show that electrical lighting loads can be reduced by 20–77% if good daylighting practices are implemented [8], [9], [10], [11], [12], [13], [14], [15], [16]. As a result, any question addressing the dilemma of daylighting design in the building sector plays a crucial role in saving energy in the U.S.

Toplights as energy efficient strategies can be used in both renovations and new designs because a one-storey building is a common practice in the U.S. Although toplights can be installed on the top floor of multi-storey buildings, they are an essential daylighting strategy for one-storey buildings. According to the Environmental Information Administration (EIA) in 2003, one-storey buildings make up 67% of commercial buildings (non-mall) in the U.S. [7]. In addition, six percent of all commercial buildings in the U.S. are vacant of which 67% are one-storey [7]. This giant sector of buildings presents a significant market that can take advantage of toplighting strategies, which eventually bring quality of life and save energy. Hence, toplights have the potential to save energy consumption in the American commercial building sector.

Considering the potential role of toplights in saving energy, the objective of this paper is to propose an algorithm that is able to optimize Skylight Floor area Ratio with regard to energy performance. In this paper the proposed algorithm uses different simulation tools and optimizes results of those simulations. Here, we used Python language within Grasshopper for writing scripts to attain daylighting results from Ladybug and Honeybee and deliver them to EnergyPlus software for energy simulations. To assess daylighting performance, we applied horizontal illuminance [lux] as a daylighting metric. Apart from integrating daylighting and energy tools, we implemented Python to script an optimization method of gradient descent to find an optimal SFR. Then, by a parametric analysis we simulated all possible SFRs and compared them with regard to total source energy consumption. The parametric analysis was used in order to verify the result of optimization method. We examined both methods on a case study which is a small office building in San Francisco. Then, we evaluated the optimal SFR against other SFRs in regard to its energy and daylighting performances.

The paper is organized as following: a review of relevant literature is presented in the next section. Section 3 demonstrates the applied simulation tools and an algorithm to integrate those tools with the purpose to reflect impacts of daylighting on total energy performance. Furthermore, in this section we introduce the optimization method in order to discover the most energy efficient SFR. In Section 4, we present a prototype office model to examine the integration algorithm and the proposed optimization method. We apply exhaustive search, parametric analysis, on the case study to verify the proposed SFR by the optimization method. Section 5 compares energy performance of different SFR scenarios by breaking down total energy consumption into electrical lighting, fan, heating and cooling loads. Moreover, those scenarios are compared based on their daylighting performance. Finally, Section 6 summarizes the results of the paper and highlights the outcome that plays a crucial role in optimizing fenestration.

Section snippets

Literature review

The importance of toplights mentioned in the previous section has motivated researchers to conduct studies which we subdivide into three major categories: studies of sidelights, individual case studies of toplights as well as initial attempts for optimization of fenestration. While the first category summarizes the studies of sidelights including windows and their impacts on lighting and HVAC loads, the second category reviews toplights and their energy performance. Both of the first and second

Methodology

This section is divided into three major subsections. In the first subsection we discuss all simulation tools and engines used in this paper. The second subsection illustrates an integration method for the applied tools and all required details needed for such integration. In the third subsection we introduce an optimization method and elaborate on how to incorporate that with the proposed integration algorithm. We will expand on all three subsections in the following paragraphs.

Case study

While the goal of this study was to set up an algorithm that could be applicable for any design projects, we simulated a base model and proposed models to validate the integration and optimization processes of this algorithm. The base model is a simple box representing a one-storey office building in San Francisco. The base model lacked any sidelight because in this study we were concerned about the impacts of skylights on energy performance. The proposed models was built upon the base model

Results and discussion

While the optimization method and parametric analysis reached the same optimal SFR, both required different iterations. Fig. 8 shows energy performance of six alternatives generated by the optimization method. It started with the random first SFR of 20% and it took six alternatives for the optimization algorithm to converge on 5.5% SFR as the optimal one. In addition, Fig. 9 presents energy performance of all alternatives by the parametric analysis method and demonstrates that 6% is the optimal

Conclusion

In this study we proposed and examined the integrative algorithm and optimization method to calibrate the size of skylights based on their total source energy consumption including electrical lighting and HVAC loads. Through two methods of parametric analysis as well as gradient decent optimization we concluded that the optimal skylight floor area is 5.5–6% for a one-storey office building in a San Francisco climate. In addition to the optimization of skylight area, we induced that the

Acknowledgment

The authors would like to thank Prof. Atila Novoselac for his encouragement and valuable insights, as well as materials of his courses that he offers the department of Civil, Architectural and Environmental Engineering at The University of Texas at Austin.

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