The increasing trend in energy demand is higher than the one from renewable generation, in the coming years. One of the greatest sources of consumption are buildings. The energy management of a building by means of the production of photovoltaic energy in situ is a common alternative to improve sustainability in this sector. An efficient trade-off of the photovoltaic source in the fields of Zero Energy Buildings (ZEB), nearly Zero Energy Buildings (nZEB) or MicroGrids (MG) requires an accurate forecast of photovoltaic production. These systems constantly generate data that are not used. Artificial Intelligence methods can take advantage of this missing information and provide accurate forecasts in real time. Thus, in this manuscript a comparative analysis is carried out to determine the most appropriate Artificial Intelligence methods to forecast photovoltaic production in buildings. On the one hand, the Machine Learning methods considered are Random Forest (RF), Extreme Gradient Boost (XGBoost), and Support Vector Regressor (SVR). On the other hand, Deep Learning techniques used are Standard Neural Network (SNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). The models are checked with data from a real building. The models are validated using normalized Mean Bias Error (nMBE), normalized Root Mean Squared Error (nRMSE), and the coefficient of variation (R2). Standard deviation is also used in conjunction with these metrics. The results show that the models forecast the test set with errors of less than 2.00% (nMBE) and 7.50% (nRMSE) in the case of considering nights, and 4.00% (nMBE) and 11.50% (nRMSE) if nights are not considered. In both situations, the R2 is greater than 0.85 in all models.

https://www.mdpi.com/2076-3417/12/17/8769/pdf?version=1662011667

Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting

Effective data management strategy and RDD weight cache replacement strategy in Spark

Breast cancer is diagnosed using histopathological imaging. This task is extremely time-consuming due to high image complexity and volume. However, it is important to facilitate the early detection of breast cancer for medical intervention. Deep learning (DL) has become popular in medical imaging solutions and has demonstrated various levels of performance in diagnosing cancerous images. Nonetheless, achieving high precision while minimizing overfitting remains a significant challenge for classification solutions. The handling of imbalanced data and incorrect labeling is a further concern. Additional methods, such as pre-processing, ensemble, and normalization techniques, have been established to enhance image characteristics. These methods could influence classification solutions and be used to overcome overfitting and data balancing issues. Hence, developing a more sophisticated DL variant could improve classification accuracy while reducing overfitting. Technological advancements in DL have fueled automated breast cancer diagnosis growth in recent years. This paper reviewed studies on the capability of DL to classify histopathological breast cancer images, as the objective of this study was to systematically review and analyze current research on the classification of histopathological images. Additionally, literature from the Scopus and Web of Science (WOS) indexes was reviewed. This study assessed recent approaches for histopathological breast cancer image classification in DL applications for papers published up until November 2022. The findings of this study suggest that DL methods, especially convolution neural networks and their hybrids, are the most cutting-edge approaches currently in use. To find a new technique, it is necessary first to survey the landscape of existing DL approaches and their hybrid methods to conduct comparisons and case studies.

/pdf/accuracy-analysis-of-deep-learning-methods-in-breast-cancer-30ry6gdp.pdf

Accuracy Analysis of Deep Learning Methods in Breast Cancer Classification: A Structured Review

Modern release engineering practices such as continuous integration and delivery have allowed software development companies to transition from a long release cycle to a shorter one. The shorter release cycle has led to more software releases available to customers. At the same time, companies developing high-volume software-intensive embedded systems often deliver patch releases and maintenance releases on top of major and minor releases to customers who pick and choose what releases apply to them and decide when to upgrade the system, if to upgrade at all. While release engineering has been studied before in web-based, desktop-based, and embedded software, the focus has been on pre-release activities. Few studies have investigated what happens after the release, particularly the role of tracing software from release to deployment in high-volume software-intensive embedded systems. To address this gap, we conducted a qualitative case study at a multi-national telecommunications systems provider focusing on Radio Access Network (RAN) software. RAN software is a complex and large-scale embedded software used in mobile networks Base Stations (BS), providing software functionality for RAN mobile technologies ranging from 2G to 5G. Our study shed light on post-release software traceability and how it is used in the release engineering process. 

The Role Of Post-Release Software Traceability in Release Engineering: A Software-Intensive Embedded Systems Case Study From The Telecommunications Domain

There is abundant observational data in the software engineering domain, whereas running large-scale controlled experiments is often practically impossible. Thus, most empirical studies can only report statistical correlations -- instead of potentially more insightful and robust causal relations. To support analyzing purely observational data for causal relations, and to assess any differences between purely predictive and causal models of the same data, this paper discusses some novel techniques based on structural causal models (such as directed acyclic graphs of causal Bayesian networks). Using these techniques, one can rigorously express, and partially validate, causal hypotheses; and then use the causal information to guide the construction of a statistical model that captures genuine causal relations -- such that correlation does imply causation. We apply these ideas to analyzing public data about programmer performance in Code Jam, a large world-wide coding contest organized by Google every year. Specifically, we look at the impact of different programming languages on a participant's performance in the contest. While the overall effect associated with programming languages is weak compared to other variables -- regardless of whether we consider correlational or causal links -- we found considerable differences between a purely associational and a causal analysis of the very same data. The takeaway message is that even an imperfect causal analysis of observational data can help answer the salient research questions more precisely and more robustly than with just purely predictive techniques -- where genuine causal effects may be confounded.

Towards Causal Analysis of Empirical Software Engineering Data: The Impact of Programming Languages on Coding Competitions

/pdf/data-management-for-production-quality-deep-learning-models-wb7y5foa.pdf

Data management for production quality deep learning models: Challenges and solutions

Chapter 1 Climbing the Stairway to Heaven

Continuous deployment has become a widely used practice in web-based software applications. Deploying a new software version to production is a seamless automated process executed thousands of times per day. Continuous deployment reduces the time between a code commit and that commit is active in production. While continuous deployment promises many advantages to software development organizations, the adoption of continuous deployment in the software-intensive embedded systems industry is limited. Several empirical studies have highlighted the challenges associated with software-intensive embedded systems. However, very few studies, if any at all, have attempted to provide a practical approach to realize continuous deployment to these systems. This paper proposes a Controlled Continuous Deployment (CCD) approach, which considers the constraints software-intensive embedded systems have, such as high reliability and availability requirements, limited possibility for rollback after deployment, and the high volume of in-service systems in the market. We derived the approach by conducting a case study at Ericsson AB, focusing on three Radio Access Networks (RAN) technologies embedded software used in 3G, 4G, and 5G mobile networks.

Controlled Continuous Deployment: A Case Study From The Telecommunications Domain

Artificial intelligence (AI) and the use of machine learning (ML) and deep learning (DL) technologies are becoming increasingly popular in companies. These technologies enable companies to leverage big quantities of data to improve system performance and accelerate business development. However, despite the appeal of ML/DL, there is a lack of systematic and structured methods and processes to help data scientists and other company roles and functions to develop, deploy and evolve models. In this paper, based on multi‐case study research in six companies, we explore practices and challenges practitioners experience in developing ML/DL models as part of large software‐intensive embedded systems. Based on our empirical findings, we derive a conceptual framework in which we identify three high‐level activities that companies perform in parallel with the development, deployment and evolution of models. Within this framework, we outline activities, iterations and triggers that optimize model design as well as roles and company functions. In this way, we provide practitioners with a blueprint for effectively integrating ML/DL model development into the business to achieve better results than other (algorithmic) approaches. In addition, we show how this framework helps companies solve the challenges we have identified and discuss checkpoints for terminating the business case.

/pdf/towards-an-ai-driven-business-development-framework-a-multi-1p51bpga.pdf

Towards an AI‐driven business development framework: A multi‐case study

—Randomised ﬁeld experiments, such as A/B testing, have long been the gold standard for evaluating software changes. In the automotive domain, running randomised ﬁeld experiments is not always desired, possible, or even ethical. In the face of such limitations, we develop a framework BOAT ( B ayesian causal modelling for O bvserv A tional T esting), utilising observational studies in combination with Bayesian causal inference, in order to understand real-world im-pacts from complex automotive software updates and help software development organisations arrive at causal conclusions. In this study, we present three causal inference models in the Bayesian framework and their corresponding cases to address three commonly experienced challenges of software evaluation in the automotive domain. We develop the BOAT framework with our industry collaborator, and demonstrate the potential of causal inference by conducting empirical studies on a large ﬂeet of vehicles. Moreover, we relate the causal assumption theories to their implications in practise, aiming to provide a comprehensive guide on how to apply the causal models in automotive software engineering. We apply Bayesian propensity score matching for producing balanced control and treatment groups when we do not have access to the entire user base, Bayesian regression discontinuity design for identifying covariate dependent treatment assignments and the local treatment effect, and Bayesian difference-in-differences for causal inference of treatment effect overtime and implicitly control unobserved confounding factors. Each one of the demonstrative case has its grounds in practise, and is a scenario experienced when randomisation is not feasible. With the BOAT framework, we enable online software evaluation in the automotive domain without the need of a fully randomised experiment.

Jan Bosch

Papers

Data management for production quality deep learning models: Challenges and solutions

Chapter 1 Climbing the Stairway to Heaven

Controlled Continuous Deployment: A Case Study From The Telecommunications Domain

Towards an AI‐driven business development framework: A multi‐case study

Bayesian causal inference in automotive software engineering and online evaluation