Evolutionary Multi-objective Optimization in Building Retrofit Planning Problem

AbstractEnergy efficiency has been a primary subject of concern in the building sector, which consumes the largest portion of the world's total energy. Especially for existing buildings, retrofitting has been regarded as the most feasible and cost-effective method to improve energy efficiency. When planning retrofit in public buildings, the most obvious objectives are to: (1) minimize energy consumption; (2) minimize CO2 emissions; (3) minimize retrofit costs; and (4) maximize thermal comfort; and one must consider these concerns together. The aim of this study is to apply evolutionary multi-objective optimization algorithm (NSGA-III) that can handle four objectives at a time to the application of building retrofit planning. A brief description of the algorithm is given, and the algorithm is examined using a building retrofit project, as a case study. The performance of the algorithm is evaluated using three measures: average distance to true Pareto-optimal front, hypervolume, and spacing. The results show that this study could be used to find a comprehensive set of trade-off scenarios for all possible retrofits, thereby providing references for building retrofit planners. These decision makers can then select the optimal retrofit strategy to satisfy stakeholders’ preferences

Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model

Accurate prediction of the energy consumption of government-owned buildings in the design phase is vital for government agencies, as it enables formulation of the early phases of development of such buildings with a view to reducing their environmental impact. The aim of this study was to identify the variables that are associated with energy consumption in government-owned buildings and to propose a predictive model based on those variables. The proposed approach selects relevant variables using the RReliefF variable selection algorithm. The support vector machine (SVM) method is used to develop a model of energy consumption based on the identified variables. The proposed approach was analyzed and validated on data for 175 government-owned buildings derived from the 2003 Commercial Building Energy Consumption Survey (CBECS) database. The experimental results revealed that the proposed model is able to predict the energy consumption of government-owned buildings in the design phase with a reasonable level of accuracy. The proposed model could be beneficial in guiding government agencies in developing early strategies and proactively reducing the environmental impact of a building, thereby achieving a high degree of sustainability of buildings constructed for government agencies

Detection of Internal Defects in As-Built Pipelines for Structural Health Monitoring: A Sensor Fusion Approach Using Infrared Thermography and 3D Laser-Scanned Data

Internal defects of pipelines are among the main factors causing accidents in the production phase of industrial plants. Periodic monitoring of a pipeline’s inner surface condition is of great importance for minimizing the risk of failure of industrial plants. This study proposes a sensor fusion approach to detect internal defects automatically in as-built pipelines during their service lives to ensure structural safety. The proposed approach uses infrared thermography combined with threedimensional (3D) laser-scanned data. For this purpose, a multi-sensor system equipped with a thermal infrared camera and a 3D laser scanner was internally and externally calibrated. From the combined data set, 3D points corresponding to the as-built pipelines are extracted from laser-scanned data. Then, thermographic analysis of the corresponding thermal data of those pipelines is performed. In this step, the local thermal gradients on the pipeline’s surface are calculated to detect areas having different thermal values. In addition, the global thermal gradients along the longitudinal or radial axes of the pipeline are calculated to determine the consistency of its internal thickness. The field experiment was performed at an operating petrochemical plant to validate the proposed approach. The experimental results revealed that the proposed approach has potential for detecting internal defects in as-built pipelines from infrared thermography combined with 3D laser-scanned data

Knowledge-Based Approach for 3D Reconstruction of As-Built Industrial Plant Models from Laser-Scan Data

The three-dimensional (3D) reconstruction of as-built industrial plant models plays an important role in revamping planning, maintenance planning, and preparation for dismantling during the lifecycle of industrial plants. Recently, the 3D reconstruction of existing industrial plants was conducted using laserscan data to make surveying processes more efficient. However, the current 3D reconstruction process from laser-scan data is still limited due to the need for significant human assistance. Although a great deal of effort has been made to efficiently reconstruct 3D as-built industrial plant models, the presence of objects—such as equipment, pipelines, and valves of different sizes and shapes—in existing industrial plants significantly increases the complexity of laser-scan data and makes automating the reconstruction process more challenging in practice. The purpose of this study is to propose a knowledge-based approach for the 3D reconstruction of as-built industrial plant models from unstructured laser-scan data. First, pipelines were extracted from laser-scan data based on surface curvature information and knowledge about pipelines' sizes from existing piping and instrumentation diagrams (P&ID). Once entire pipelines were extracted, they were modeled based on skeleton features. Then, the remaining objects were clustered and grouped separately via the region grouping process. Afterward, clustered objects were retrieved and modeled based on global feature-based matching from the 3D database. Finally, the resulting model was checked to ensure that it was well-reconstructed according to the information regarding the relationships among objects abstracted from the existing P&ID. The preliminary results on actual industrial plants show that integrating knowledge into the reconstruction steps played an important role in the proposed approach and that this approach obtained accurate as-built industrial plant models from unstructured laser-scan data. The proposed approach could be successfully utilized to assist in many applications during the lifecycle of industrial plants

A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources

Automatic 3D Reconstruction of As-built Pipeline Based on Curvature Computations from Laser-Scanned Data

Demand has been growing for three-dimensional (3D) reconstruction of asbuilt pipelines that occupy large areas within operating plants. In practice, measurements are efficiently performed using laser-scanning technology; however reconstructing an as-built pipeline from this laser-scanned data remains challenging. The data acquired from the plant facility can be incomplete due to complex occlusion, or it can be affected by noise due to the reflective surfaces of the pipelines and other parts. The aim of this study is to propose a method for generating models of entire pipelines that include straight pipes, elbows, reducers, and tee pipes from laserscanned data. The proposed 3D reconstruction method for as-built pipelines is divided into three main tasks: (1) identifying the types and locations of the pipelines from the laser-scanned data; (2) segmenting the pipelines into each type of pipe form; and (3) reconstructing the pipelines’ geometry and topology and generating models of them. Field experiments were performed at an operating industrial plant in order to validate the proposed method. The results revealed that the proposed method can indeed contribute to the automation of 3D reconstruction of as-built pipelines

Automated pipeline extraction for modeling from laserscanned data

Purpose Threedimensional (3D) as-built plant models are required for various purposes, such as plant operation, maintenance, and the expansion of existing facilities. The as-built plant model reconstruction process consists of as-built plant measurement and 3D plant model reconstruction. As-built plant measurement uses 3D laser scanning technology to efficiently acquire data. However, the current method used for 3D as-built plant model reconstruction from laserscanned data is still labor-intensive. The objective of this study is to develop a fully-automated parametric reconstruction of the as-built pipe-line occupying a large portion of the area in an as-built plant. Method The proposed approach consists of three main steps. The first step is to extract the cylindricallyformed pipelines from laser-scanned data based on random sampling consensus (RANSAC). The second step is to segment the extracted pipelines into pipe components, such as straight pipe, elbow, and branch tee, based on medial axis extraction and curve skeletonization. The last step is to surface-model reconstruct the segmented pipe-lines using the parametric modeling method. Results & Discussion The experiment was performed at an operating plant to validate the proposed method. The experimental results revealed that the proposed method could contribute to automation for 3D as-built plant model reconstruction