IoT Based Smart Home Automation Using Solar Photovoltaic System and Online Time Server

2021 ◽  
Author(s):  
Chiradeep Ghosh ◽  
Somdeb Chanda ◽  
Kashmira Sil
Keyword(s):  
Smart Home ◽  
Photovoltaic System ◽  
Home Automation ◽  
Solar Photovoltaic ◽  
Solar Photovoltaic System

scholarly journals Reinforcement Learning-Based Energy Management of Smart Home with Rooftop Solar Photovoltaic System, Energy Storage System, and Home Appliances

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3937 ◽  
Author(s):  
Sangyoon Lee ◽  
Dae-Hyun Choi
Keyword(s):  
Energy Storage ◽  
Smart Home ◽  
Storage System ◽  
Energy Storage System ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Air Conditioner ◽  
Indoor Temperature ◽  
Q Learning ◽  
Solar Photovoltaic System

This paper presents a data-driven approach that leverages reinforcement learning to manage the optimal energy consumption of a smart home with a rooftop solar photovoltaic system, energy storage system, and smart home appliances. Compared to existing model-based optimization methods for home energy management systems, the novelty of the proposed approach is as follows: (1) a model-free Q-learning method is applied to energy consumption scheduling for an individual controllable home appliance (air conditioner or washing machine), as well as the energy storage system charging and discharging, and (2) the prediction of the indoor temperature using an artificial neural network assists the proposed Q-learning algorithm in learning the relationship between the indoor temperature and energy consumption of the air conditioner accurately. The proposed Q-learning home energy management algorithm, integrated with the artificial neural network model, reduces the consumer electricity bill within the preferred comfort level (such as the indoor temperature) and the appliance operation characteristics. The simulations illustrate a single home with a solar photovoltaic system, an air conditioner, a washing machine, and an energy storage system with the time-of-use pricing. The results show that the relative electricity bill reduction of the proposed algorithm over the existing optimization approach is 14%.

scholarly journals Self-Sustained Autonomous Wireless Sensor Network with Integrated Solar Photovoltaic System for Internet of Smart Home-Building (IoSHB) Applications

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 653
Author(s):  
Md. Rokonuzzaman ◽  
Mahmuda Khatun Mishu ◽  
Nowshad Amin ◽  
Mithulananthan Nadarajah ◽  
Rajib Baran Roy ◽  
...  
Keyword(s):  
Energy Demand ◽  
Smart Home ◽  
Data Transfer ◽  
Photovoltaic System ◽  
Wireless Sensor ◽  
Solar Photovoltaic ◽  
Home Building ◽  
Ambient Data ◽  
Self Powered ◽  
Solar Photovoltaic System

Conventional wireless sensor networks (WSNs) in smart home-building (SHB) are typically driven by batteries, limiting their lifespan and the maximum number of deployable units. To satisfy the energy demand for the next generation of SHB which can interconnect WSNs to make the internet of smart home-building (IoSHB), this study introduces the design and implementation of a 250 mW to 2.3 W energy harvesting device. The proposed device is dynamically autonomous owing to the integration of embedded solar photovoltaic (PV) modules and power storage through a supercapacitor (SC; 5 V, 0.47 F) capable of powering WSNs for 95 s (up to 4.11 V). The deployed device can harvest indoor and outdoor ambient light at a minimum illumination of 50 lux and a maximum illumination of 200 lux. Moreover, the proposed system supports wireless fidelity (Wi-Fi) and Bluetooth Low Energy (BLE) to do data transfer to a webserver as a complete internet of things (IoT) device. A customized android dashboard is further developed for data monitoring on a smartphone. All in all, this self-powered WSN node can interface with the users of the SHBs for displaying ambient data, which demonstrates its promising applicability and stability.

scholarly journals A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Varaprasad Janamala
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Voltage Stability ◽  
Optimal Allocation ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Voltage Profile ◽  
Pv System ◽  
Solar Photovoltaic System ◽  
The Impact

AbstractA new meta-heuristic Pathfinder Algorithm (PFA) is adopted in this paper for optimal allocation and simultaneous integration of a solar photovoltaic system among multi-laterals, called interline-photovoltaic (I-PV) system. At first, the performance of PFA is evaluated by solving the optimal allocation of distribution generation problem in IEEE 33- and 69-bus systems for loss minimization. The obtained results show that the performance of proposed PFA is superior to PSO, TLBO, CSA, and GOA and other approaches cited in literature. The comparison of different performance measures of 50 independent trail runs predominantly shows the effectiveness of PFA and its efficiency for global optima. Subsequently, PFA is implemented for determining the optimal I-PV configuration considering the resilience without compromising the various operational and radiality constraints. Different case studies are simulated and the impact of the I-PV system is analyzed in terms of voltage profile and voltage stability. The proposed optimal I-PV configuration resulted in loss reduction of 77.87% and 98.33% in IEEE 33- and 69-bus systems, respectively. Further, the reduced average voltage deviation index and increased voltage stability index result in an improved voltage profile and enhanced voltage stability margin in radial distribution systems and its suitability for practical applications.

A High Gain Novel Double-Boost Converter for DC Microgrid Applications

2020 ◽  
Vol 29 (15) ◽  
pp. 2050246 ◽  
Author(s):  
B. N. Ch. V. Chakravarthi ◽  
G. V. Siva Krishna Rao
Keyword(s):  
Control Strategies ◽  
High Gain ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Dc Microgrid ◽  
Boost Converters ◽  
Power Point ◽  
Solar Photovoltaic System ◽  
Conversion Stage ◽  
And Control

In solar photovoltaic (PV)-based DC microgrid systems, the voltage output of the classical DC–DC converter produces very less voltage as a result of poor voltage gain. Therefore, cascaded DC–DC boost converters are mandatory for boosting the voltage to match the DC microgrid voltage. However, the number of devices utilized in the DC–DC conversion stage becomes higher and leads to more losses. Thereby, it affects the system efficiency and increases the complication of the system and cost. In order to overcome this drawback, a novel double-boost DC–DC converter is proposed to meet the voltage in DC microgrid. Also, this paper discusses the detailed operation of maximum power point (MPP) tracking techniques in the novel double-boost DC–DC converter topology. The fundamental [Formula: see text]–[Formula: see text] and [Formula: see text]–[Formula: see text] characteristics of solar photovoltaic system, operational details of MPP execution and control strategies for double-boost DC/DC converter are described elaborately. The proposed converter operation and power injection into the DC microgrid are verified through the real-time PSCAD simulation and the validation is done through the experiment with hardware module which is indistinguishable with the simulation platform.

A novel way of parameter estimation of solar photovoltaic system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rahul Bisht ◽  
Afzal Sikander
Keyword(s):  
Parameter Estimation ◽  
Mean Absolute Error ◽  
Optimization Technique ◽  
Absolute Error ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Solar Panels ◽  
Content Type ◽  
Solar Photovoltaic System

Purpose This paper aims to achieve accurate maximum power from solar photovoltaic (PV), its five parameters need to be estimated. This study proposes a novel optimization technique for parameter estimation of solar PV. Design/methodology/approach To extract optimal parameters of solar PV new optimization technique based on the Jellyfish search optimizer (JSO). The objective function is defined based on two unknown variables and the proposed technique is used to estimate the two unknown variables and the rest three unknown variables are estimated analytically. Findings In this paper, JSO is used to estimate the parameters of a single diode PV model. In this study, eight different PV panels are considered. In addition, various performance indices, such as PV characteristics, such as power-voltage and current-voltage curves, relative error (RE), root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE) are determined using the proposed algorithm and existing algorithms. The results for different solar panels have been obtained under varying environmental conditions such as changing temperature and constant irradiance or changing irradiance and constant temperature. Originality/value The proposed technique is new and provides better results with minimum RE, RMSE, NMAE, MAE and converges fast, as depicted by the fitness graph presented in this paper.

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