Automatic Control using Fuzzy Techniques for Energy Management on Smart Building

Iksan, Nur; Udayanti, Erika Devi; Arfriandi, Arief; Widodo, Djoko Adi

doi:10.1109/cenim.2018.8711385

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Indonesia, energy usage has reached 157,992 GWh, and most of it comes from fossil fuels (coal and petroleum). Several strategic steps have been taken, namely by developing energy management systems [1] [2] and Non-intrusive Load Monitoring (NILM) systems to monitor and control the use of electricity [3]. The next step is to use non-fossil / renewable energy (solar cells, wind, water, etc.)…”

Section: Introductionmentioning

confidence: 99%

Zigbee Communication on Machine to Machine Smart Home Micro Grid for Efficient Controlling System

Purwanto¹,

Hermawan²,

Suherman³

2020

Proceedings of the Proceedings of the 13th International Interdisciplinary Studies Seminar, IISS 2019, 30-31 October 2019, Mala

View full text Add to dashboard Cite

Data communication between sensors and actuator devices with users in the energy monitoring and control system on the smart grid is very dependent on the existence of a gateway device. This causes the control process to be ineffective because it is very dependent on the Gateway as the communication center. For that we need an effective approach so that the communication process can take place only on the relevant device, i.e. using M2M technology. In its development, the use of M2M technology has challenges related to congestion, error communication and delay so that further research is needed. In this research the development of a smart home micro grid will use M2M technology which enables communication between devices in the smart grid area. One component needed in M2M communication is Zigbee which is a medium for data communication without wires. Zigbee performance is measured using RSSI and Throughput with scenarios without or with obstructions as well as scenarios through the selection of router configurations. Experimental results show optimal performance of zigbee with scenarios in obstacle areas at a distance of 35 meters and in areas without barriers at a distance of 60 meters. In the router configuration selection scenario, it can be concluded that direct configuration is the optimal choice rather than using a router. For this reason, when using a configuration through a router, the number of sending nodes must be minimized.

show abstract

Section: Introductionmentioning

confidence: 99%

Zigbee Communication on Machine to Machine Smart Home Micro Grid for Efficient Controlling System

Purwanto¹,

Hermawan²,

Suherman³

2020

Proceedings of the Proceedings of the 13th International Interdisciplinary Studies Seminar, IISS 2019, 30-31 October 2019, Mala

View full text Add to dashboard Cite

show abstract

“…Table 1 provides an overview of the energy management methods cited in this paragraph, including their most important features or weakness. In this way, the FLC proposed in [93] aimed to design a BEMS using cloud computing. The FLC was integrated into a cloud service, providing the BEMS with the following features: automation, and intelligent monitoring services, through both the web and through smartphones.…”

mentioning

confidence: 99%

Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management

et al. 2021

View full text Add to dashboard Cite

During the last decade, the smart grid (SG) concept has started to become a reality, mainly thanks to the technical progress achieved in telecommunications, informatics and power electronics, among other domains, leading to an evolution of the traditional electrical grid into an intelligent one. Nowadays, the SG can be seen as a system of smart systems that include cyber and physical parts from different technologies that interact with each other. In this context, intelligent buildings (IBs) constitute a paradigm in which such smart systems are able to guarantee the comfort of residents while ensuring an appropriate tradeoff of energy production and consumption by means of an energy management system (EMS). These interconnected EMSs remain the objective of potential cyber-attacks, which is a major concern. Therefore, this paper conducts a survey, from a multidisciplinary point of view, of some of the main security and privacy issues related to IBs as part of the SG, including an overview of EMS, smart meters, and the main communication networks employed to connect IBs to the overall SG. Future research directions towards a security enhancement from both technical and human perspectives are also provided.

show abstract

Renewable energy power generation forecasting using deep learning method

Widodo

Iksan

Udayanti

et al. 2021

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Smart Micro Grid in household areas aims to meet electricity needs through the integration between state power plant with renewable energy sources so that the electricity used does not depend entirely on state utility. Smart Micro Grid also enables the availability of energy management services supported by Machine Learning (ML) technology, Big Data, Artificial Intelligence (AI), Internet of Things (IoT) and smart sensors so that consumer use of electricity is more efficient. To improve energy management services and distribution of renewable energy sources, new innovations in ML technology are needed to produce accurate learning models that can be used in the energy analysis process, such as monitoring, prediction, forecasting, scheduling and decision-making. However, the complexity of the problems in the smart grid system, which includes uncertainty and non-linearity, affects the more complex the energy data structure generated. Therefore, the simple ML method will not be able to perform the Learning process because it is limited to simple raw data processing. Therefore, the Deep Learning (DL) method can be used as a Learning method on data that has a complex and large structure. In this paper, Deep Neural Network (DNN) method will be developed using Long Short-Term Memory (LSTM) as a Learning model to provide Future Accurate Prediction (FAP) on electricity use and on renewable energy plants. Prediction test using Confusion Matrix accuracy value and RMSE error value

show abstract

Automatic Control using Fuzzy Techniques for Energy Management on Smart Building

Cited by 3 publications

References 11 publications

Zigbee Communication on Machine to Machine Smart Home Micro Grid for Efficient Controlling System

Zigbee Communication on Machine to Machine Smart Home Micro Grid for Efficient Controlling System

Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management

Renewable energy power generation forecasting using deep learning method

Contact Info

Product

Resources

About