scholarly journals A Generic Pipeline for Machine Learning Users in Energy and Buildings Domain

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5410
Author(s):  
Mahmoud Abdelkader Bashery Abbass ◽  
Mohamed Hamdy
Keyword(s):  
Machine Learning ◽  
Real Life ◽  
Real Data ◽  
Management Systems ◽  
Full Description ◽  
Energy Management Systems ◽  
Fitting Algorithm ◽  
Best Fitting ◽  
The Right ◽  
Or Applications

One of the biggest problems in applying machine learning (ML) in the energy and buildings field is the lack of experience of ML users in implementing each ML algorithm in real-life applications the right way, because each algorithm has prerequisites to be used and specific problems or applications to be implemented. Hence, this paper introduces a generic pipeline to the ML users in the specified field to guide them to select the best-fitting algorithm based on their particular applications and to help them to implement the selected algorithm correctly to achieve the best performance. The introduced pipeline is built on (1) reviewing the most popular trails to put ML pipelines for the energy and building, with a declaration for each trial drawbacks to avoid it in the proposed pipeline; (2) reviewing the most popular ML algorithms in the energy and buildings field and linking them with possible applications in the energy and buildings field in one layout; (3) a full description of the proposed pipeline by explaining the way of implementing it and its environmental impacts in improving energy management systems for different countries; and (4) implementing the pipeline on real data (CBECS) to prove its applicability.

scholarly journals Experiment strategy for evaluating advanced building energy management system

2021 ◽  
Vol 2042 (1) ◽  
pp. 012030
Author(s):  
Hanmin Cai ◽  
Fazel Khayatian ◽  
Philipp Heer
Keyword(s):  
Energy Management ◽  
Experimental Studies ◽  
Real Life ◽  
Active Role ◽  
Building Energy ◽  
Management Systems ◽  
Low Carbon ◽  
Energy Management Systems ◽  
Building Energy Management ◽  
Readiness Level

Abstract Buildings are envisioned to play an active role in future low-carbon energy systems. The complexity of building energy management systems increases as they interface more and more subsystems and domains. As an important step to achieve a higher technology readiness level, these energy management systems need to be systematically tested in real-life conditions. Currently, there are no standard testing and experiment strategies in buildings to handle the mentioned complexity. Additionally, the levels of details reported in the existing experimental studies are heterogeneous. This paper summarizes an application of a holistic testing method to a flexible fully-equipped prosumer with the goal of facilitating test preparation, execution, replication, and comparison. Several empirical suggestions are provided, and a hybrid quantification strategy with digital twins is presented.

Privacy as Protection of the Incomputable Self: From Agnostic to Agonistic Machine Learning

2019 ◽  
Vol 20 (1) ◽  
pp. 83-121 ◽  
Author(s):  
Mireille Hildebrandt
Keyword(s):  
Machine Learning ◽  
Research Design ◽  
Real Life ◽  
Big Data Analytics ◽  
Validity And Reliability ◽  
Human Identity ◽  
The Right To Privacy ◽  
Trade Offs ◽  
Life Problems ◽  
The Right

Abstract This Article takes the perspective of law and philosophy, integrating insights from computer science. First, I will argue that in the era of big data analytics we need an understanding of privacy that is capable of protecting what is uncountable, incalculable or incomputable about individual persons. To instigate this new dimension of the right to privacy, I expand previous work on the relational nature of privacy, and the productive indeterminacy of human identity it implies, into an ecological understanding of privacy, taking into account the technological environment that mediates the constitution of human identity. Second, I will investigate how machine learning actually works, detecting a series of design choices that inform the accuracy of the outcome, each entailing trade-offs that determine the relevance, validity and reliability of the algorithm’s accuracy for real life problems. I argue that incomputability does not call for a rejection of machine learning per se but calls for a research design that enables those who will be affected by the algorithms to become involved and to learn how machines learn — resulting in a better understanding of their potential and limitations. A better understanding of the limitations that are inherent in machine learning will deflate some of the eschatological expectations, and provide for better decision-making about whether and if so how to implement machine learning in specific domains or contexts. I will highlight how a reliable research design aligns with purpose limitation as core to its methodological integrity. This Article, then, advocates a practice of “agonistic machine learning” that will contribute to responsible decisions about the integration of data-driven applications into our environments while simultaneously bringing them under the Rule of Law. This should also provide the best means to achieve effective protection against overdetermination of individuals by machine inferences.

scholarly journals Energy Management Systems for Grid-Connected Houses with Solar PV and Battery by Considering Flat and Time-of-Use Electricity Rates

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5028
Author(s):  
Xincheng Pan ◽  
Rahmat Khezri ◽  
Amin Mahmoudi ◽  
Amirmehdi Yazdani ◽  
GM Shafiullah
Keyword(s):  
Energy Management ◽  
Operating Cost ◽  
Electricity Consumption ◽  
Real Data ◽  
Management Systems ◽  
Solar Pv ◽  
Rule Based ◽  
Energy Management Systems ◽  
Electricity Rates ◽  
Pv Generation

This paper develops new practical rule-based energy management systems (EMSs) for typical grid-connected houses with solar photovoltaic (PV) and battery by considering different rates for purchasing and selling electricity. The EMSs are developed to supply the household’s loads and reduce operating costs of the system based on different options of flat and time-of-use (ToU) rates for buying and selling electricity prices. Four different options are evaluated and compared in this study: (1) Flat-Flat, (2) ToU-Flat, (3) Flat-ToU, and (4) ToU-ToU. The operation cost is calculated based on the electricity exchange with the main grid, the equivalent cost of PV generation, as well as the degradation cost of battery storage. The operation of the grid-connected house with rooftop solar PV and battery is evaluated for a sunny week in summer and a cloudy week in winter to investigate the proper performance for high and low generations of PV. While the developed rule-based EMS are generic and can be applied for any case studies, a grid-connected house in Australia is examined. For this purpose, real data of solar radiation, air temperature, electricity consumption, and electricity rates are used. It is found that the ToU-Flat option has the lowest operating cost for the customers.

scholarly journals Driver Behavior Classification System Analysis Using Machine Learning Methods

2021 ◽  
Vol 11 (22) ◽  
pp. 10562
Author(s):  
Raymond Ghandour ◽  
Albert Jose Potams ◽  
Ilyes Boulkaibet ◽  
Bilel Neji ◽  
Zaher Al Barakeh
Keyword(s):  
Machine Learning ◽  
System Analysis ◽  
Real Life ◽  
Real Data ◽  
Gradient Boosting ◽  
Machine Learning Classification ◽  
The Road ◽  
Machine Learning Methods ◽  
Boosting Method ◽  
On The Road

Distraction while driving occurs when a driver is engaged in non-driving activities. These activities reduce the driver’s attention and focus on the road, therefore increasing the risk of accidents. As a consequence, the number of accidents increases and infrastructure is damaged. Cars are now equipped with different safety precautions that ensure driver awareness and attention at all times. The first step for such systems is to define whether the driver is distracted or not. Different methods are proposed to detect such distractions, but they lack efficiency when tested in real-life situations. In this paper, four machine learning classification methods are implemented and compared to identify drivers’ behavior and distraction situations based on real data corresponding to different behaviors such as aggressive, drowsy and normal. The data were randomized for a better application of the methods. We demonstrate that the gradient boosting method outperforms the other used classifiers.

Machine Learning in Mobile Crowd Sourcing: A Behavior-Based Recruitment Model

2022 ◽  
Vol 22 (1) ◽  
pp. 1-28
Author(s):  
Menatalla Abououf ◽  
Shakti Singh ◽  
Hadi Otrok ◽  
Rabeb Mizouni ◽  
Ernesto Damiani
Keyword(s):  
Machine Learning ◽  
Real Life ◽  
Task Completion ◽  
Behavioral Models ◽  
Crowd Sourcing ◽  
Behavioral Profiling ◽  
The Right ◽  
Mobile Crowd ◽  
Selection Of

With the advent of mobile crowd sourcing (MCS) systems and its applications, the selection of the right crowd is gaining utmost importance. The increasing variability in the context of MCS tasks makes the selection of not only the capable but also the willing workers crucial for a high task completion rate. Most of the existing MCS selection frameworks rely primarily on reputation-based feedback mechanisms to assess the level of commitment of potential workers. Such frameworks select workers having high reputation scores but without any contextual awareness of the workers, at the time of selection, or the task. This may lead to an unfair selection of workers who will not perform the task. Hence, reputation on its own only gives an approximation of workers’ behaviors since it assumes that workers always behave consistently regardless of the situational context. However, following the concept of cross-situational consistency, where people tend to show similar behavior in similar situations and behave differently in disparate ones, this work proposes a novel recruitment system in MCS based on behavioral profiling. The proposed approach uses machine learning to predict the probability of the workers performing a given task, based on their learned behavioral models. Subsequently, a group-based selection mechanism, based on the genetic algorithm, uses these behavioral models in complementation with a reputation-based model to recruit a group of workers that maximizes the quality of recruitment of the tasks. Simulations based on a real-life dataset show that considering human behavior in varying situations improves the quality of recruitment achieved by the tasks and their completion confidence when compared with a benchmark that relies solely on reputation.

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