Introduction The essential of Internet of Things in nowadays have become the key topic in technology Industry
The essential of Internet of Things in nowadays have become the key topic in technology
Industry. This expertise is embodied on a wide spectrum of networked systems, products and sensors allowing these systems to work to altogether to generate, exchange, consume data with minimal human intervention. Moreover, smart energy is a major witness to energy saving where by the use of smart meters plays a significant role to which will lead to energy saving from these different buildings for the environment and global sustainability. The smart meter is one such device which may be considered as the first example of IOT’s that is capable of transferring electrical consumption metering details of buildings on real time basis to electricity service providers. Further this meter can also transfer equipment wise recording of the consumption pattern in case the building is equipped with smart devices. Because of this capability of a smart meter adoption time of smart electrical devices installed in a conflict can recorded very timely and efficiently. That these recording if mined following the mining techniques may provide useful adoption patterns that may remain unknown otherwise. The existing electrical meter does not offer this facility of detecting and recording electrical consumption of different electrical devices.
With the growth of the population, business and industry, electricity supply is sometimes under pressure, particularly in densely populated urban areas when usage reaches a peak, resulting in a power outage.
This study follows the IoT technology and creates a conceptual structure intended to serve as a support or guide for building an energy saving infrastructure for different technologies through collection of data using information technology.
South African Smart Grid Initiative (SASGI) under guidance of Department of Energy has proposed a Smart Grid Vision 2030 for the Electricity Supply Industry. This research therefore identifies the opportunity for the implementation of Smart Grid Vision 2030 as part of the proposed IoT framework. According to https://phys.org/news/2018-01-smart-electricity.html#jCp a smart grid ensures that users get a top-notch service.
As part of this study, various articles from various researchers have been analysed for the purpose of reviewing literature on IOT based on smart energy.
Jianli Pan*, Raj Jain, Subharthi Paul?, Tam Vu§, Abusayeed Saifullah#, Mo Sha (2015) have done research on IOT framework for Smart Energy in Buildings. The research done by these authors addressed factors such as; (1) the energy consumption data analysis, (2) new smart location-based automated energy control framework designs, and (3) experimental prototype that applies IoT networking and computing technologies to improve the energy efficiency in buildings.
According to the research done by Shashi Kant Srivastava (2014) it was identified that once smart meters are part of society, huge data will be generated with the potential to mine useful insights, not only for manufacturers of smart devices, owners of electricity supply, government for policy formation, but also to the owners of the houses consuming electricity, to bring the consumption to more efficient and economical levels.
Based on the research done by Srujana Uddanti1, Christeena Joseph2, P.C.Kishoreraja (2017) IOT-based meter reading system is designed to monitor the meter reading and service provider can disconnect the power source whenever the customer does not pay the monthly bill and also it eliminates the human involvement in Energy meter management. Their research has achieved following goals: theft detection at buyer end in real time, LCD displays energy consumption units and amount and disconnection of service from remote server.
A research made by Chinmaya Mahapatra 1, * ID, Akshaya Kumar Moharana 2 and Victor C. M. Leung 1 ID (2017) shows that cities today face diverse challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation.
According to the research done by Kemal Akkaya?, Ismail Guvenc?, Ramazan Aygun†, Nezih Pala?, Abdullah Kadri ‡ (2017) the current approaches indicated a trend towards the use of existing IoTs that are available within the buildings. With the goal of using minimal hardware and software costs, future smart buildings have a great potential to save energy by employing smart control strategies on HVAC through the help of data collected via IoT.
Literature review has shown that these researchers have proposed the IOT frameworks for smart energy, however a gap still exists regarding the incorporation of smart grid into the framework for the purpose of allowing an efficient and effective control and monitoring of the energy usage and supply. This study therefore bridges the gap by proposing a framework that has the smart grid as part of the IOT framework.
The Proposed IOT Framework
Figure 2: Proposed IOT Framework
Collection of Data
The studies done that proves that using the traditional method of monitoring frequency on the power grid is inadequate to be used as the only method for optimizing electricity usage. A good data collection strategy should be affordable, accurate, adaptable, continuous, and timely.
This study shows the benefits of collecting data using in Internet of Things (IoT) – smart energy and how collection of data can reduce the amount of energy consumption for most electrical services if implemented correctly. There are several ways suggested to improve both energy efficiency through collection of data in smart energy/meter (Zhao, Peffer, Narayanamurthy, and Ram, 2016 et al).
Due to a recent push for reducing electricity consumption and increasing operational ef?ciency, building managers need to deal with dynamic and diverse requirements of buildings including anomaly detection, predictive maintenance, occupancy tracking, and electricity usage optimization with renewable integration (Tushar, Wijerathne, Yuen, Poor, Saha, and Wood, 2018).
Data can be collected from buildings using different strategies:
It is possible to collect massive amount of data using devices such as smartphones, sensors, cameras, and Radio-Frequency Identification (RFIDs), meters and actuators. Akkaya, Guvenc, Aygun, Pala and Kadri (2015) also indicates that the use of Heating Ventilation and Air Conditioning (HVAC) is an extensive opportunity to improve energy consumption of buildings via smart meter control.
This can be done in two ways:
(1) Using the existing infrastructure in the buildings besides requiring installation apps or it could be done using apps in the user’s smart devices.
(2) Developing effective data fusion techniques for improving occupancy monitoring accuracy using a multitude of sources.
Diagram below elaborates on how this study propose to collect data
Fig. 1: Shows the use of sensor fusion and machine learning for IoT based green building management.
Different positioning in the building indicated to collect data and usage of energy in various directions of electrical activities in the building as it indicated in the diagram. Camera, lights, motion and temperature can all in a sensation manner by detecting building occupancy, therefore data will be collected intelligently to the smart meter. The use of IoTs in commercial buildings and data collection in building occupants and environment can be monitored in real time. Real-time access to occupancy counts in different zones.
Sensors are playing an important part in the occupancy of the building as for they manage to detect human presence acknowledging light, camera, motion all most everything. Sensor s detects dark and intelligent lights to switches on.
What this means is that the success of these activities will depend on the quality, accuracy, and usability of data that is collected
Transportation of Data
The Wireless communication will be employed due to the different size of communcation coverage and the variety of utilities. There shall be a division of three major domains in the smart grid namely the home area network(HAN), neighborhood area network (NAN) and the wide area network(WAN). This is to support the high capacity for computing in smartgrid and also a central unit which should be taken into consideration as an additional in the future wireless communication architecture as indicated in the figure below.
For the purpose of saving the energy consumed the home area network (HAN) will consist of a range of smart appliances that is to collect the real-time power information of smart appliances through the wireless communication. The data collected from the smart metering reading is all gathered to generate the total energy consumption information and is transmitted between the home area network and neighbourhood are network. Alike concepts to home area network are the industrial area network and building area network- which are practical to industrial and commercial situations. Furthermore, the wireless area network is responsible for collecting information from multiple neighbourhood area network and is transmitted to the central unit which consist of a control centre and data centre for centralized management.
In order to improve the wireless communication quality, the technologies namely cloud and cloudlet, crowdsourcing and cache control is important to be implemented as part the smart grid. The cloud and cloudlet technology the power should receive real-time management ranging from power generation to power distribution. Crowdsourcing technology can be defined as a way of resources being shared between SMs for the sake of common interests. The Cache control technology is effective as to relieve high real-time volume to improve the wireless communication quality.
The cloud storage will be used to store data from the smart meter because IoT data can be generated quite rapidly, the volume of data can be huge and the types of data can be various. Web-based database application will be created within cloud to store, share and access data via internet using different devices. The cloud is the technology suitable for filtering, analysing and storing information in useful ways.
Organizing and updating data through web-based database will be much easier because it will obtain a single view of all data by accessing data distributed across the database, Provides high availability plans, with seamless rolling security updates for your cloud database. The cloud effectively serves as the brain to improved decision-making and optimized internet-based interactions. When IoT meets cloud, new challenges arise. The critical concerns during integration are quality of service (QoS) and quality of experience (QoE), as well as data security, privacy and reliability. Cloud Platform offers a range of storage solutions from unstructured blobs of data to structured entity storage of devices or transactions, and high-performance key-value databases for event and telemetry data. Cloud provides a single API for both current-use object storage, and archival data that is used infrequently. If IoT device captures data, Cloud Storage can store virtually unlimited quantities durably and economically. The combination of cloud computing and IoT will enable new monitoring services and powerful processing of sensory data streams.
Cloud computing is available for use anytime and anywhere so long as the device is connected to the internet based on the software as a service (SaaS), platform as a service (PaaS), or infrastructure as a service (IaaS) service model.
The proposed IOT framework suggests that collected data be first stored prior to analysing it. This framework consists of three key areas for data analysis, namely; 1) Load Analysis, 2) Load Forecasting, and 3) Load Management.
Figure 2: Data Analysis
1. Load Analysis
The different consumers’ load profiles are diverse on different days. It is important to understand the volatility and uncertainty of the massive load profiles for further load analysis. In the proposed IOT framework, the load analysis is reviewed from the perspective of anomaly detection and load profiling. Anomaly detection is of vital importance because training a model such as a forecasting model or clustering model on a smart meter dataset with anomalous data may result in bias or failure for parameter estimation and model establishment. Moreover, reliable smart meter data are important for accurate billing. The anomaly detection in smart meter data is summarized from the perspective of bad data detection and energy theft detection. Load profiling is used to find the basic electricity consumption patterns of each consumer or a group of consumers. The load profiling results can be further used for load forecasting and demand response programs.
2. Load Forecasting
Load forecasts have been widely used by the electric power industry. Power distribution companies rely on short- and long-term forecasts at the feeder level to support operations and planning processes. The higher level the load is measured at, the smoother the load profile typically is. Developing a highly accurate forecast is nontrivial at lower levels. Although the majority of the load forecasting literature has been devoted to forecasting at the top (high voltage) level, the information from medium/low voltage levels, such as distribution feeders and even down to the smart meters, offer some opportunities to improve the forecasts. A load forecasting in this framework focuses on the transition from point load forecasting to probabilistic load forecasting. In this study, review will be done for both point and probabilistic load forecasting with the emphasis on the medium/low voltage levels. Within the point load forecasting, the study will review whether the smart meter data is used or not.
3. Load Management
How smart meter data contribute to the implementation of load management is summarized from three aspects in this framework: the first one is to have a better understanding of sociodemographic information of consumers to provide better and personalized service. The second one is to target the potential consumers for demand response program marketing. The third one is the issue related to demand response program implementation including price design for price-based demand response and baseline estimation for incentive-based demand response.
A Smart Grid is an electricity network that can intelligently integrate the actions of all users connected to it – generators, consumers and those that do both – in order to efficiently deliver sustainable, economic and secure electricity supplies.
Below is the proposed Smart Grid from the SmartGrid2030 Vision draft:
Smart Grid technology approach enables the load controllable management and this topic was reviewed fully by Jingshuang Shen, Chuanwen Jiang * and Bosong Ling in the ISSN 1996-1073 their published journal. The document states that with by implementing the smart grid technology we do not only have the advantage of peak shaving, load balance, frequency regulation, and voltage stability, but it is also effective at providing fast balancing services to the renewable energy grid in the distributed power system as opposed to the historical South African electricity supply infrastructure in which the emergency generation can be called and there is still not enough electricity for the load demand, then they engage a pattern of load shedding, a system contractually pre-arranged with some very big power users like steel mills and aluminium smelters. These customers agree to have their power switched off for short times in an emergency so that everyone else doesn’t have to experience a power interruption, or blackout.
Our framework focus on dynamic load control scheme for smart grid systems. This can be achieved through the data analysis part on the proposed IoT framework with Smart Energy. Traditionally, the customers sign the interruptible load contracts with the utility companies and then reduce demand at the fixed time when the system is at the peak load period or at any time requested by the power utility. In the smart grid environment, however, these controllable devices can communicate with the upper control system or the distributor operation company, and the bi-level mutual information is communicated in real-time. Measurements from the controllable loads are sent to the management centre through a two-way communication network, and the customers provide various ancillary services with demand response management(DSM).