The Experts below are selected from a list of 150456 Experts worldwide ranked by ideXlab platform

Shanlin Yang - One of the best experts on this subject based on the ideXlab platform.

  • big data driven smart energy management from big data to big insights
    Renewable & Sustainable Energy Reviews, 2016
    Co-Authors: Kaile Zhou, Shanlin Yang
    Abstract:

    Large amounts of data are increasingly accumulated in the energy sector with the continuous Application of Sensors, wireless transmission, network communication, and cloud computing technologies. To fulfill the potential of energy big data and obtain insights to achieve smart energy management, we present a comprehensive study of big data driven smart energy management. We first discuss the sources and characteristics of energy big data. Also, a process model of big data driven smart energy management is proposed. Then taking smart grid as the research background, we provide a systematic review of big data analytics for smart energy management. It is discussed from four major aspects, namely power generation side management, microgrid and renewable energy management, asset management and collaborative operation, as well as demand side management (DSM). Afterwards, the industrial development of big data-driven smart energy management is analyzed and discussed. Finally, we point out the challenges of big data-driven smart energy management in IT infrastructure, data collection and governance, data integration and sharing, processing and analysis, security and privacy, and professionals.

Kaile Zhou - One of the best experts on this subject based on the ideXlab platform.

  • big data driven smart energy management from big data to big insights
    Renewable & Sustainable Energy Reviews, 2016
    Co-Authors: Kaile Zhou, Shanlin Yang
    Abstract:

    Large amounts of data are increasingly accumulated in the energy sector with the continuous Application of Sensors, wireless transmission, network communication, and cloud computing technologies. To fulfill the potential of energy big data and obtain insights to achieve smart energy management, we present a comprehensive study of big data driven smart energy management. We first discuss the sources and characteristics of energy big data. Also, a process model of big data driven smart energy management is proposed. Then taking smart grid as the research background, we provide a systematic review of big data analytics for smart energy management. It is discussed from four major aspects, namely power generation side management, microgrid and renewable energy management, asset management and collaborative operation, as well as demand side management (DSM). Afterwards, the industrial development of big data-driven smart energy management is analyzed and discussed. Finally, we point out the challenges of big data-driven smart energy management in IT infrastructure, data collection and governance, data integration and sharing, processing and analysis, security and privacy, and professionals.

Sulayman Lezma - One of the best experts on this subject based on the ideXlab platform.

  • The data driven workplace: Exploring the Application of sensor data for facility managers in an Activity Based Working environment (ABW)
    2020
    Co-Authors: Sulayman Lezma
    Abstract:

    Motivation. As technological developments in the workplace accelerate, workplaces are connecting to a variety of Sensors, actuators, and dedicated networks in the Internet of Things (IoT). Sensor data can be collected on aspects of workplace performance and occupants’ behaviors. While the performance of the workplace is evaluated, the validation by facility managers is under-development and dominated by perceptual self-report measurements. In Post Occupancy Evaluation (POE) the main instrument used by facility managers is surveys. Due to the properties of the instruments, surveys and other self-report measurements raise concern for the effectiveness of verifications to maintain and improve the performance of the workplace environment. With new ways of working, Activity-Based Working (ABW) is a promising workstyle focused on people, place, and technology, but several performance issues have been identified. Limited studies focus on the Application of sensor data, and IoT is only recently recognized to make a significant impact on the validation of the workplace performance by facility managers. Research aim and goal. The main research aim is to explore the Application of sensor data for the validation of the ABW performance by facility managers. The research goal is to identify the opportunities and limitations of the current sensor data in an IoT from a Facility Management (FM) perspective. Research method. The exploration of the sensor data is conducted utilizing both research and design in a case study. The case study is a prominent smart building located at Schiphol that has embraced the concept of ABW. In the case study, the available sensor data is collected and explored for opportunities and limitations. Components of an interface are designed from a FM perspective to explore the sensor data structurally in an ABW environment. The exploration of the data is conducted using hypotheses. Three hypotheses are formulated based on performance issues in ABW from literature, and further specified based on the case study. Key findings and conclusion. The exploration of the sensor data illustrated that the role and decision-making process of a facility manager can drastically change within the ABW environment of smart buildings. The facility manager can validate the workplace performance with goals set for each activity. Facility managers can shift from a complaint-driven reactive attitude to a proactive data-driven analytic of the workplace performance. With the sensor data, the facility manager can additionally rely on continuous quantitative and objective data of the workplace performance. The use of both subjective inputs from occupants’ perception in combination with objective sensor data can improve the reliability of the data for more effective verifications in the ABW environment. Nevertheless, the current state of smart buildings is not up to expectations. The sensor data is the critical input in an IoT and lacks on many fronts. Researchers and practitioners are encouraged to align the data of the Sensors with KPIs from a facility management perspective by (re)considering the functionality of the Sensors. To benchmark the sensor data with aspects of employee satisfaction, productivity, and well-being, a consensus is required amongst organizations for the Application of Sensors with similar output values.Architecture, Urbanism and Building Science

Mansour Matloobi - One of the best experts on this subject based on the ideXlab platform.

  • heat transfer and mlp neural network models to predict inside environment variables and energy lost in a semi solar greenhouse
    Energy and Buildings, 2016
    Co-Authors: Morteza Taki, Yahya Ajabshirchi, Seyed Faramarz Ranjbar, Abbas Rohani, Mansour Matloobi
    Abstract:

    Abstract The greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve. There are many control methods, such as adaptive, feedback and intelligent control and they require a precise model. Therefore, many modeling methods have been proposed for this purpose, including physical, transfer function and black-box modeling. The main goal of this paper is to compare some mathematical models (include dynamic and multiple linear regression (MLR)) with innovative method (Artificial Neural Network) and select the best one to predict inside air and roof temperature (Ta and Tri) and energy lost in a semi-solar greenhouse in Iran. For this purpose, a semi-solar greenhouse was designed and constructed at the North-West of Iran in Azerbaijan Province (geographical location of 38°10′ N and 46°18′ E with elevation of 1364 m above the sea level). The environment factors influencing the Ta and Tri include outside air temperature (To), wind speed (vo), solar radiation on the roof (Io), inside soil temperature (Ts) and inside air humidity (RHa), which were all collected as data samples. Then through the relationship between the factors, 4 main factors were extracted, and the relationship between the main factors and the original data was discussed by MLP and MLR models. Results showed that the Durbin–Watson statistic for MLR method to estimate Ta and Tri was 0.04 and 0.06 respectively, so this method cannot predict the output parameters correctly. Comparing MLP and dynamic models showed that the performance of MLP model was better according to small values of RMSE and MAPE and large value of EF indices. Statistical comparisons of the predicted data by neural network models and the actual data of the inside air and roof temperature showed that there is no significant difference between them. Also, the minimum value of the TSSE was 16.68 and 30.87 (°C2) for Ta and Tri in ANN implementation. The performance of best model (MLP) to estimate the energy lost and exchange in a semi-solar greenhouse showed that MLP method is applicable to estimate the real data in greenhouse and then predict the energy lost and exchange. This method can be used online in greenhouses to decrease some cost related to Application of Sensors and some record instruments.

Sadhu, Pradip Kumar - One of the best experts on this subject based on the ideXlab platform.

  • Erratum: Smart Grid Modernization: Opportunities and Challenges
    'IntechOpen', 2021
    Co-Authors: Dhara Saumen, Shrivastav, Alok Kumar, Sadhu, Pradip Kumar
    Abstract:

    Recently, there have been significant technological approaches for the bulk power grid. The customer demand is associated with conventional grid coupled large central generating stations through a high voltage transmission to a distribution system. Urban transmission systems are consistently progressing to meet the increasing needs for power and to replace old-pattern generation with native renewable generation and power provisions from outward green energy resources. Power grid is undergoing remarkable modernization towards advanced consistency, greater efficiency, and less cost by the incorporation of renewable energy and developed control technology. Quick developing nature of grid, consumer needs, and industrial invention situates substation modernization at the leading of grid transformation. Smart grid is essential to accomplish all the fastest technological reformations occurring in generation, transmission and distribution (T&D) of electric power, with growing Application of Sensors, computers and communications. In this study the recent trend and Application of electric power grid is briefly enunciated