State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Multi-time-horizon Solar Forecasting Using Recurrent Neural Network

The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation. This research paper proposes a unified architecture for multi-time-horizon predictions for short and long-term solar forecasting using Recurrent Neural Networks (RNN). The paper describes an end-to-end pipeline to implement the architecture along with the methods to test and validate the performance of the prediction model. The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon. The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE 2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy

This paper presents a novel mechanism to adapt surrogate-assisted population-based algorithms. This mechanism is applied to ACM-ES, a recently proposed surrogate-assisted variant of CMA-ES. The resulting algorithm, saACM-ES, adjusts online the lifelength of the current surrogate model (the number of CMA-ES generations before learning a new surrogate) and the surrogate hyper-parameters. Both heuristics significantly improve the quality of the surrogate model, yielding a significant speed-up of saACM-ES compared to the ACM-ES and CMA-ES baselines. The empirical validation of saACM-ES on the BBOB-2012 noiseless testbed demonstrates the efficiency and the scalability w.r.t the problem dimension and the population size of the proposed approach, that reaches new best results on some of the benchmark problems.Comment: Genetic and Evolutionary Computation Conference (GECCO 2012) (2012

Managing the costs of new product development projects: a longitudinal case study at an automotive company

The overarching research topic of this dissertation is the management of the costs of new product development projects. New product development (hereinafter NPD) is essential for most companies, as the introduction of innovative products is crucial for their long-term success. Due to the high level of uncertainty that comes with the innovative process of product development, the management of NPD costs is a highly challenging task. We illuminate the field of NPD cost management from two perspectives, which represent our research topics. The first research topic of this thesis is the estimation of NPD costs. NPD costs are costs triggered by the activities that companies pursue to technically develop new products (i.e., labor costs of engineers, project managers, designers, and quality assessors, costs for tools and software required in NPD, costs of material and components required for testing and prototyping, and NPD-related overhead costs). Many authors have presented methods for product cost estimation in general, mostly focusing on overall product costs or direct material costs. Limited research is available about the estimation of the specific cost type of NPD costs. We conduct three studies to contribute to this gap. First, we give an overview of the status quo regarding NPD cost estimation. We do this by conducting a systematic literature review on methods for this purpose. Second, we develop and present the NPD cost benchmarking method. With this method, which is mostly built on external data, we add a new approach to the literature on NPD cost estimation methods. As third study in the context of NPD cost estimation, we present a case study in which we provide detailed, empirical insights on the challenges in NPD cost estimation, and on the application of the NPD cost benchmarking method in particular. The second research topic of this thesis concerns decision-making processes during NPD projects. In this uncertain and dynamic environment, decision-makers often rely on heuristics to choose between alternative options for responding to unpredicted developments during NPD projects (for example, changes in market demands, technical challenges, or new information about competitors). Empirical insights are mostly missing about how such decisions are made. Our fourth study provides insights on the use of heuristics in ongoing NPD project managerial decision-making by conceptualizing and empirically testing the within-project NPD cost compensation heuristic. This dissertation was supervised by Prof. Dr. Marc Wouters from KIT’s chair of Management Accounting at the Institute of Management. It is written in English language and the author aims to obtain the title of Dr. rer. pol

Unsupervised machine learning approach for building composite indicators with fuzzy metrics

[EN] This study aims at developing a new methodological approach for building composite indicators, focusingon the weight schemes through an unsupervised machine learning technique. The composite indicatorproposed is based on fuzzy metrics to capture multidimensional concepts that do not have boundaries, suchas competitiveness, development, corruption or vulnerability. This methodology is designed for formativemeasurement models using a set of indicators measured on different scales (quantitative, ordinal and binary)and it is partially compensatory. Under a benchmarking approach, the single indicators are synthesized.The optimization method applied manages to remove the overlapping information provided for the singleindicators, so that the composite indicator provides a more realistic and faithful approximation to the conceptwhich would be studied. It has been quantitatively and qualitatively validated with a set of randomizeddatabases covering extreme and usual cases.This work was supported by the project FEDER-University of Granada (B-SEJ-242.UGR20), 2021-2023: An innovative methodological approach for measuring multidimensional poverty in Andalusia (COMPOSITE). Eduardo Jimenez-Fernandez would also like to thank the support received from Universitat Jaume I under the grant E-2018-03.Jiménez Fernández, E.; Sánchez, A.; Sánchez Pérez, EA. (2022). Unsupervised machine learning approach for building composite indicators with fuzzy metrics. Expert Systems with Applications. 200:1-11. https://doi.org/10.1016/j.eswa.2022.11692711120