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Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

This comparative study examines the moderating role of risk management in project planning and project success in the construction business of the UK and Pakistan. The data was gathered from 152 project managers (76 from both economies each) using a survey questionnaire. The purposive sampling technique was used to ensure fair representation of sample size and the RAND formula was used to select the project managers. For quantitative analysis, partial least square structural equation modelling technique was utilised. The results confirmed that project planning had a statistically significant impact on project success. Furthermore, risk management significantly moderated the relationship between project planning and project success in the construction businesses despite being in two different economies. The paper contributes to the enhancement of the body of knowledge intended for global companies and academicians aiming to implement risk management frameworks to enhance project success and ensure the effectiveness of project planning in a competitive business environment. It offers a new perspective to investigate the relationship between project planning and project success through moderating the effect of risk management, which is a new theoretical dimension for construction business and the field of project management. It is proposed to the governments and construction businesses operating in the UK and Pakistan to validate the empirical research framework in the cross-cultural context while assessing risk and uncertainty. It helps the construction business in the evaluation of risk while planning and successfully implementing project strategies.