• Energy Research

Predicting the duration of construction projects with acceptable accuracy is a problem for contractors and researchers. Numerous researchers and tools are involved in sorting out this problem. The aim of the study is to predict the construction duration using four analytical tools as an approach. The success of construction projects in regard to time depends on various factors such as selection of contractors, consultants, cost of the projects, quality of the projects, the quantity of the projects, environmental factors, etc. Presently available commercial tools in the market are not designed as universally common and concerned. Every tool performs well in a particular situation. The prediction of India’s highway road projects duration is the biggest construction issue in the country due to various reasons. To overcome this problem, the methodology of the paper adopts various strategies to find suitable tools to predict the highway road projects’ duration, in which it classifies and analyzes the collected data. As a part of this work, the details of 363 government infrastructure projects (traditional procurement) were collected from 2000 to 2018. The present study also adopts various tools for duration prediction such as artificial neural networks (ANNs), smoothing techniques, time series analysis, and Bromilow’s time–cost (BTC) model. The results of the study recommend smoothing techniques with a constant value of 0.3, which gave the remarkable very small error of 1.2%, and its outcomes become even better when compared to other techniques.

Predicting the duration of construction projects with acceptable accuracy is a problem for contractors and researchers. Numerous researchers and tools are involved in sorting out this problem. The aim of the study is to predict the construction duration using four analytical tools as an approach. The success of construction projects in regard to time depends on various factors such as selection of contractors, consultants, cost of the projects, quality of the projects, the quantity of the projects, environmental factors, etc. Presently available commercial tools in the market are not designed as universally common and concerned. Every tool performs well in a particular situation. The prediction of India’s highway road projects duration is the biggest construction issue in the country due to various reasons. To overcome this problem, the methodology of the paper adopts various strategies to find suitable tools to predict the highway road projects’ duration, in which it classifies and analyzes the collected data. As a part of this work, the details of 363 government infrastructure projects (traditional procurement) were collected from 2000 to 2018. The present study also adopts various tools for duration prediction such as artificial neural networks (ANNs), smoothing techniques, time series analysis, and Bromilow’s time–cost (BTC) model. The results of the study recommend smoothing techniques with a constant value of 0.3, which gave the remarkable very small error of 1.2%, and its outcomes become even better when compared to other techniques.

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.

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.

The objective of this study was to investigate the factors that influence the consumer adoption of renewable energy in Thailand. The study adopted an extended theory of planned behavior (TPB) by including three additional variables. The study applied a quantitative study methodology, with primary data collected using a survey of consumers in five major cities in Thailand. The data were analyzed using structural equation modeling (SEM). The findings of the study indicated that perception of self-effectiveness, environmental concern, renewable energy awareness, and beliefs about renewable energy benefits have a significant and positive effect on consumers’ intention to adopt renewable energy. The cost of renewable was found to have a negative but non-significant influence on consumers’ adoption of renewable energy, while risk/trust perception was found to have a positive but non-significant influence on consumers’ adoption of renewable energy. The study concluded that stakeholders should take into account the aspects of perception of self-effectiveness, environmental concern, renewable energy awareness, and beliefs about renewable energy benefits when running campaigns to promote the consumer adoption of renewable energy in Thailand

The objective of this study was to investigate the factors that influence the consumer adoption of renewable energy in Thailand. The study adopted an extended theory of planned behavior (TPB) by including three additional variables. The study applied a quantitative study methodology, with primary data collected using a survey of consumers in five major cities in Thailand. The data were analyzed using structural equation modeling (SEM). The findings of the study indicated that perception of self-effectiveness, environmental concern, renewable energy awareness, and beliefs about renewable energy benefits have a significant and positive effect on consumers’ intention to adopt renewable energy. The cost of renewable was found to have a negative but non-significant influence on consumers’ adoption of renewable energy, while risk/trust perception was found to have a positive but non-significant influence on consumers’ adoption of renewable energy. The study concluded that stakeholders should take into account the aspects of perception of self-effectiveness, environmental concern, renewable energy awareness, and beliefs about renewable energy benefits when running campaigns to promote the consumer adoption of renewable energy in Thailand

Information technology advancements integrated with the e-commerce supply chain allow participants in the business process to effectively work with large volumes of data and control transactions. To improve the profitability and competitiveness of e-commerce companies, a blockchain solution was incorporated into the global B2B (Business-to-Business) supply chain. This technology simplified the transaction process by providing all participants in the sustainable B2B buying process with the same data about the trade. Overall, the use of blockchain improved the efficiency of logistics and digital documentation which reached 74% and 75%, respectively. The main advantage of using blockchain is that it creates a decentralized database that is secure. In addition, it increases the speed of payment and the reliability and transparency of data transfer. Further research may focus on the use of blockchain in green logistics to improve environmental sustainability in the e-commerce supply chain.