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.

Big data driven smart energy management: From big data to big insights

The long way toward multivariate predictive control of flotation processes

Building energy use is expected to grow by more than 40% in the next 20 years. Electricity remains the largest energy source consumed by buildings, and that demand is growing. To mitigate the impact of the growing demand, strategies are needed to improve buildings' energy efficiency. In residential buildings home appliances, water, and space heating are answerable for the increase of energy use, while space heating and other miscellaneous equipment are behind the increase of energy utilization in non-residential buildings. Building energy management systems support building managers and proprietors to increase energy efficiency in modern and existing buildings, non-residential and residential buildings can benefit from building energy management system to decrease energy use. Base on the type of building, different management strategies can be used to achieve energy savings. This paper presents a review of management strategies for building energy management systems for improving energy efficiency. Different management strategies are investigated in non-residential and residential buildings. Following this, the reviewed researches are discussed in terms of the type of buildings, building systems, and management strategies. Lastly, the paper discusses future challenges for the increase of energy efficiency in building energy management system.

A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis

The load data in smart grid contains a lot of valuable knowledge, which is useful for both electricity producers and consumers. Load classification is an important issue in load data mining. A five-stage process model of load classification is constructed based on the summary and analysis of studies about load classification in smart grid environment. Then, the commonly used clustering methods for load classification are summarized and briefly reviewed, and the well-known evaluation methods for load classification are also introduced. Besides, the applications of load classification, including bad data identification and correction, load forecasting and tariff setting, are discussed. Finally, an example of load classification based on Fuzzy c-means (FCM) is presented.

A review of electric load classification in smart grid environment

Some of the ideas associated with process control systems have already been discussed in §1.4. The majority of processes may be controlled automatically by adjusting the rate of flow of energy or material entering the process. In manually-controlled systems the human operator adjusts the rate of flow by means of a hand-operated valve; he will receive information regarding the state of the process from a measuring instrument giving a visual indication. Manual control has numerous disadvantages and indeed manual control of many modern plants would be virtually impossible, owing to their complexity and the speed and precision of control they require for successful operation.

Process Control Systems

Building load management using cluster and statistical analyses

In this paper, the effects of network delay among interconnecting elements of a distributed computer network control system are analyzed. Experimental tests have been accomplished to show the influence of the combined effects of the network delay (between sensor/controller and controller/actuator) on the overall performance of a feedback control system. For this purpose, a proportional-plus-integral (PI) controller has been used. This controller has been designed, using the well-known direct synthesis method and implemented in a didactic networked control system platform. Corresponding (z-transform) difference equations have been used, assuming time-triggered (regular) samplings. The augmented PI controller (with compensation of the control signal and estimation of missing measurements) is developed. Stability analysis is performed to verify the validity of this scheme. The results presented in the paper show that the dynamic behavior of the closed-loop system can be improved using the proposed technique.

Fábio Gonçalves Jota

Papers

Building load management using cluster and statistical analyses

Design of Networked Control Systems With Explicit Compensation for Time-Delay Variations

Fuzzy detection of high impedance faults in radial distribution feeders

Control strategy for a column flotation process

Dynamic model of a flotation column