(1991). Applied Linear Regression Models. Journal of Quality Technology: Vol. 23, No. 1, pp. 76-77.

Applied Linear Regression Models

Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to achieve green cellular networks. With the development of green energy technologies, BSs are able to be powered by green energy in order to reduce the on-grid energy consumption, thus reducing the CO2 footprints. In this paper, we envision that the BSs of future cellular networks are powered by both on-grid energy and green energy. We optimize the energy utilization in such networks by maximizing the utilization of green energy, and thus saving on-grid energy. The optimal usage of green energy depends on the characteristics of the energy generation and the mobile traffic, which exhibit both temporal and spatial diversities. We decompose the problem into two sub-problems: the multi-stage energy allocation problem and the multi-BSs energy balancing problem. We propose algorithms to solve these sub-problems, and subsequently solve the green energy optimization problem. Simulation results demonstrate that the proposed solution achieves significant on-grid energy savings.

On Optimizing Green Energy Utilization for Cellular Networks with Hybrid Energy Supplies

The fifth generation (5G) of mobile communication system aims to deliver a ubiquitous mobile service with enhanced quality of service (QoS). It is also expected to enable new use-cases for various vertical industrial applications-such as automobiles, public transportation, medical care, energy, public safety, agriculture, entertainment, manufacturing, and so on. Rapid increases are predicted to occur in user density, traffic volume, and data rate. This calls for novel solutions to the requirements of both mobile users and vertical industries in the next decade. Among various available options, one that appears attractive is to redesign the network architecture-more specifically, to reconstruct the radio access network (RAN). In this paper, we present an inclusive and comprehensive survey on various RAN architectures toward 5G, namely cloud-RAN, heterogeneous cloud-RAN, virtualized cloud-RAN, and fog-RAN. We compare them from various perspectives, such as energy consumption, operations expenditure, resource allocation, spectrum efficiency, system architecture, and network performance. Moreover, we review the key enabling technologies for 5G systems, such as multi-access edge computing, network function virtualization, software-defined networking, and network slicing; and some crucial radio access technologies (RATs), such as millimeter wave, massive multi-input multi-output, device-to-device communication, and massive machine-type communication. Last but not least, we discuss the major research challenges in 5G RAN and 5G RATs and identify several possible directions of future research.

/pdf/a-comprehensive-survey-of-ran-architectures-toward-5g-mobile-ddvaz98tth.pdf

A Comprehensive Survey of RAN Architectures Toward 5G Mobile Communication System

Increasing use of renewable energy sources, liberalized energy markets and most importantly, the integrations of various monitoring, measuring and communication infrastructures into modern power system network offer the opportunity to build a resilient and efficient grid However, it also brings about various threats of instabilities and security concerns in form of cyberattack, voltage instability, power quality (PQ) disturbance among others to the complex network The need for efficient methodologies for quicker identification and detection of these problems have always been a priority to energy stakeholders over the years In recent times, machine learning techniques (MLTs) have proven to be effective in numerous applications including power system studies In the literature, various MLTs such as artificial neural networks (ANN), Decision Tree (DT), support vector machines (SVM) have been proposed, resulting in effective decision making and control actions in the secured and stable operations of the power system Given this growing trend, this paper presents a comprehensive review on the most recent studies whereby MLTs were developed for power system security and stability especially in cyberattack detections, PQ disturbance studies and dynamic security assessment studies The aim is to highlight the methodologies, achievements and more importantly the limitations in the classifier(s) design, dataset and test systems employed in the reviewed publications A brief review of reinforcement learning (RL) and deep reinforcement learning (DRL) approaches to transient stability assessment is also presented Finally, we highlighted some challenges and directions for future studies

/pdf/a-review-of-machine-learning-approaches-to-power-system-4q2b9d8uxr.pdf

A Review of Machine Learning Approaches to Power System Security and Stability

The telecommunication sector plays a significant role in shaping the global economy and the way people share information and knowledge. At present, the telecommunication sector is liable for its energy consumption and the amount of emissions it emits in the environment. In the context of off-grid telecommunication applications, off-grid base stations (BSs) are commonly used due to their ability to provide radio coverage over a wide geographic area. However, in the past, the off-grid BSs usually relied on emission-intensive power supply solutions such as diesel generators. In this review paper, various types of solutions (including, in particular, the sustainable solutions) for powering BSs are discussed. The key aspects in designing an ideal power supply solution are reviewed, and these mainly include the pre-feasibility study and the thermal management of BSs, which comprise heating and cooling of the BS shelter/cabinets and BS electronic equipment and power supply components. The sizing and optimization approaches used to design the BSs’ power supply systems as well as the operational and control strategies adopted to manage the power supply systems are also reviewed in this paper.

https://www.mdpi.com/1996-1073/8/10/10904/pdf

Sustainable Power Supply Solutions for Off-Grid Base Stations

Base stations represent the main contributor to the energy consumption of a mobile cellular network. Since traffic load in mobile networks significantly varies during a working or weekend day, it is important to quantify the influence of these variations on the base station power consumption. Therefore, this paper investigates changes in the instantaneous power consumption of GSM (Global System for Mobile Communications) and UMTS (Universal Mobile Telecommunications System) base stations according to their respective traffic load. The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site. Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to this relationship, we develop a linear power consumption model for base stations of both technologies. This paper also gives an overview of the most important concepts which are being proposed to make cellular networks more energy-efficient.

https://www.mdpi.com/1424-8220/12/4/4281/pdf

Measurements and Modelling of Base Station Power Consumption under Real Traffic Loads

Dynamic and pole-zero analysis of self-excited induction generator using a novel model with iron losses

http://josip-lorincz.com/Portals/0/Radovi%20razine%20A/WEB_On%20interdependence%20among%20transmit%20and%20consumed%20power%20of%20macro%20base%20station%20technologies.pdf

On interdependence among transmit and consumed power of macro base station technologies

Increased integration of renewable energy sources brings new challenges to the secure and stable power system operation. Operational challenges emanating from the reduced system inertia, in particular, will have important repercussions on the power system transient stability assessment (TSA). At the same time, a rise of the “big data” in the power system, from the development of wide area monitoring systems, introduces new paradigms for dealing with these challenges. Transient stability concerns are drawing attention of various stakeholders as they can be the leading causes of major outages. The aim of this paper is to address the power system TSA problem from the perspective of data mining and machine learning (ML). A novel 3.8 GB open dataset of time-domain phasor measurements signals is built from dynamic simulations of the IEEE New England 39-bus test case power system. A data processing pipeline is developed for features engineering and statistical post-processing. A complete ML model is proposed for the TSA analysis, built from a denoising stacked autoencoder and a voting ensemble classifier. Ensemble consist of pooling predictions from a support vector machine and a random forest. Results from the classifier application on the test case power system are reported and discussed. The ML application to the TSA problem is promising, since it is able to ingest huge amounts of data while retaining the ability to generalize and support real-time decisions.

https://www.mdpi.com/1996-1073/14/11/3148/pdf

Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble

https://bib.irb.hr/datoteka/365268.Sensorless_speed_detection.pdf

Goran Petrović

Papers

Measurements and Modelling of Base Station Power Consumption under Real Traffic Loads

Dynamic and pole-zero analysis of self-excited induction generator using a novel model with iron losses

On interdependence among transmit and consumed power of macro base station technologies

Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble

Sensorless speed detection of squirrel-cage induction machines using stator neutral point voltage harmonics