This paper demonstrates how a combination of Business Intelligence (BI) energy analytics and Advanced Metering Infrastructure (AMI) data can serve as a better decision support platform for Electricity Distribution Companies in Nigeria (DISCOS) to compute and account for power flows in their electrical distribution networks. The authors analyzed a sample undisclosed Distribution Transformer (DT) metering dataset collected from the AMI cloud data of one of the Nigerian DISCOS using Microsoft Power BI. Results obtained have indicated that the analytic platform can serve as a decision support tool for DISCOs in Nigeria to account for energy flows in their networks and can also be combined with power flow tools to estimate energy losses in the network.
The ongoing integration of Internet of Things (IoT) within Fifth-generation (5G) mobile ecosystem promises to provide smart and intelligent services meant to foster vertical industry digitalization. 5G-IoT private networks have become a popular choice of adoption by industry verticals in accelerating digital transformation across various sectors, thereby culminating in Industry 4.0. Research and development on 5G-IoT private networks has mainly focused on the technical aspect with less attention on the business side. This paper discusses viable business model options for 5G-IoT private networks for applications in diverse industry sectors and the attainment of economic value in terms of revenue maximization, cost minimization and sustainability.
As businesses look to boost flexibility and cut costs, cloud computing is becoming more and more popular. There is always room for improvement, despite the fact that the major cloud service providers use a pay-as-you-go pricing model and provide consumers rapid scaling up and down. CPU and memory utilisation, which represents workload, frequently varies, which causes businesses to incur extra costs and has an adverse effect on the environment. To lessen the impact of this, a Random Forest algorithm which is a supervised machine learning model is proposed for resource scheduling in cloud computing environment. This proposed model is an ensemble algorithm for regression, classification and task decisions that deal with developing decision trees. The proposed strategy is compared to existing machine learning algorithms including XGBoost, Ridge and Lasso. Experimental results show that the proposed algorithm performs better than the compared algorithms in terms of the prediction accuracy of the CPU and memory. Experimental results conducted on go ogle colaboratory using Materna Dataset shows that the proposed algorithm achieved a prediction higher accuracy of 0.1 – 45.1 % higher than the compared algorithms in terms of the R-squared (R 2 ), the Root Mean Square Error (RMSE) and lower percentage errors for Mean Absolute Percentage Error (MAPE).
Utilities offer time-of-use tariff schemes as a form of demand-side management. In this tariff structure, the cost of electricity is high during peak periods when the power system is constrained. For many users, the cost of electricity during peak periods can be costly for their operations. In this work, a load management solution is proposed together with its associated hardware implementation. The load management solution requires users to classify their loads as either critical or non-critical and set a peak period energy limit for the control. The load management strategy is demonstrated through a case study to reduce overall energy consumption and the cost of energy.
Humans (and other types of primates for that matter) have a long history in different types of dispute resolution. The expression Alternative Dispute Resolution is used to describe types of non-violent dispute resolution which do not involve the court system. There are documented historical records of alternative dispute resolution systems being used in the ancient Greek city-states as early as 400 BC. This paper provides a brief history of some of these different types of alternative dispute resolution systems and then specifically looks at how alternative dispute resolution can be transformed using digital tools. The different types of alternative dispute resolution systems include negotiation, mediation, and arbitration. Online Dispute Resolution is a specific type of alternate dispute resolution which is conducted at a distance over the internet and may include a combination of negotiation, mediation, and arbitration. Online dispute resolution systems appeared on the internet at the same time as e-commerce systems started to appear. These online dispute resolution systems gave potential users of e-commerce systems the confidence that any dispute which may arise on the e-commerce system would be dealt with fairly. The use of digital tools can transform these processes of alternative dispute resolution systems.
