# Global Energy Production Computation of a Solar-Powered Smart Home Automation System Using Reliability-Oriented Metrics

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. The Performance Ratio of Static and Mobile PV Systems

#### 1.2. Solar-Powered Internet of Things (IoT)-Based Smart Home Automation Systems

## 2. Proposed Energy Management Solution for the Smart Home Automation System

#### 2.1. Solar Tracking Module with Energy Storage Solution

#### 2.2. Home Automation Model with Energy Storage Solution and Smart Switching Relay Modules

## 3. Proposed Reliability-Oriented Metrics for Computing the Energy Production of the Solar-Powered Smart Home Automation System

#### 3.1. Reliability-Oriented Metrics Applied in the Global Energy Production Formula

_{V}denotes the number of executed test vectors, N

_{E}denotes the number of errors per test case, T

_{P}denotes the total number of test patterns, and N denotes the number of similar devices used for error detection. At the same time, its variations depend mainly on the nature of test scenarios. For instance, in the case of hardware error detection, we can quickly adapt the general formula as depicted in relation (2) [16]:

_{R}represents the total number of test rounds, and P designates the number of equipped probes during the ICT method. To include all test scenarios into one compact global equation set, we apply a unified metrics system, as described in [8], according to equation set (5):

_{G}represents the global solar test factor for mixed test scenarios, measured as the average value of all previously computed STF parameters, and n denotes the total number of STF parameters. Equally, as stated in [16], the general form of the SRF parameter is presented in Equation (6):

_{G}represents the global solar reliability factor for mixed test scenarios, measured as the average value of all previously computed SRF parameters, and n denotes the total number of SRF parameters.

_{P}represents the energy production expressed in Wh, A represents the total solar panel area in cm

^{2}, r represents the solar panel yield in percent, H represents the annual average solar radiation on static solar panels (shadings not included), and PR represents the output ratio coefficient for losses (range between 0.5 and 0.9), with a default value of 0.75). Supplementary parameter r is given by the ratio of electrical power (expressed in Wp) of one solar panel divided by the panel’s functional area. Mathematically, the yield r can be written as in Equation (9):

_{V}= 7 (test cases), we have successfully identified N

_{E}= 10 bit-flip errors. Concerning property (a), since multiple errors (burst errors) may occur within a single test vector, the number of errors detected may be greater than the number of test cases [16]. Additionally, we have (b) a number D = 4 flip flops used in the structure of a random Multiple Input Signature Register (MISR). The proposed formula is used to measure the total number of test cases T

_{P}, as presented in Equation (14) [16]:

_{V}= 7 (test cases), we have successfully identified N

_{E}= 10 calculation errors. Additionally, we have: (b) a number of T

_{P}= 10 test patterns and a number B = 10 breakpoints in our software code, meaning that all calculation errors were successfully detected using the deployed software functions. Let us proceed with computing the STF parameter, as presented in Equation (16) [16]:

_{R}= 100 test routines, a total of N

_{R}= 10 rounds for each test stage, and P = 2 probes to classify N

_{E}= 12 voltage deviations. Based on the previous configuration, the STF parameter will be computed using relation (17) [16]:

#### 3.2. Global Energy Production Formula Applied to Real-Life Scenarios

#### 3.3. Alternative Formulas for Calculating the Global Energy Production of PV systems

_{ϴ}is the optical reflection reduction factor; k

_{Q}is the quantum efficiency reduction factor; k

_{B1}is the low irradiance reduction factor; k

_{ϒ}is the module temperature reduction factor; k

_{W}is the wiring losses reduction factor; k

_{S}is the soiling factor; η

_{inv}is the inverter conversion efficiency. The losses due to the temperature of cells can be calculated, at every time step, with Equation (24) [17]:

_{C}is the temperature of the PV cells [°C]; T

_{ref}is the cell’s reference temperature [°C]. Regarding the previous formula, the reference temperature for the cell is 25 °C, and the temperature factor ${}_{\mathsf{\Upsilon}}$ is referred to the energy provided by the PV module; the value of which is a variable of the particular type of module and the semiconductor that composes the cells, usually ranging between 0.2 and 0.5. The temperature T

_{C}of the PV cell is measured as a function of ambient temperature, irradiance, and NOCT parameter and is computed with Equation (25) [17]:

_{a}is the ambient temperature (in degrees Celsius); NOCT is the nominal operating cell temperature [°C]; G

_{T}is the global irradiance on the surface of the module [kW/m

^{2}]. Most of the above-described factors are affecting the performance of PV systems. For the correlation of these variables, multiple mathematical equations are used, all of which are linked to the fundamental formula (26) [17]:

_{PV}is the amount of electricity produced by the PV system during the analysis time [kWh]; PR is the solar plant’s output ratio; P

_{n}is the plant’s nominal power, calculated in STC [kW]; G

_{STC}is the solar irradiance in STC [kW/m

^{2}]; H

_{T}is the total solar irradiation on the modules plan [kWh/m

^{2}]. Formula (26) can be used to estimate PV output over long periods (day, month, year), but it can also be utilized for instant calculations. The inverter efficiency function can then be expressed using Equation (27) [17]:

_{inv}is the inverter’s power output [kW]; Pinv, nom is the inverter’s nominal power output [kW]. The parameters chosen are the most important for PV output, and therefore it is possible to define an energy estimation formula, as presented in relation (29) [17]:

_{PV,year,est}is an estimate of the electricity produced by a 1 kWp PV system in one year [kWh]; H

_{T,hor}is the average solar irradiation of the modules over a year [kWh/m

^{2}]; T

_{m}denotes the annual average air temperature [°C], and $\mathsf{\Upsilon}$ is the power temperature coefficient [%/°C]. A reliability ratio formula is finally used to compare the measured energy production with the estimated energy production, as illustrated in Equation (30):

_{PV,year,}is the yearly measured energy PV production [kWh]; E

_{PV,year,est}is the yearly estimated PV production. The reliability ratio calculated for accurate data remains in the level of accuracy generally attributed to PV simulation tools [17].

_{Watt Peak}is the maximum power generation of the PV panel; Area Array is the usable surface equipped with PV cells; PSI is the peak sun insolation (solar radiation), and η

_{PV}represents the solar panel efficiency. For a more concrete example, let us consider that the Array area is 50 m

^{2}, PSI is rated at 1000 W/m

^{2}, and the solar panel efficiency is 17%. The above-listed assumptions were made for STC where solar cell efficiency η

_{PV}is defined between 15% and 17%, at a temperature of 25 °C, resulting in the equation set (32) [18]:

_{m}is the maximum outputted power by a solar panel.

## 4. Experimental Setup and Results

#### 4.1. Hardware Implementation and Cloud Layer of the Solar-Powered Smart Home Automation System

#### 4.2. Energy Production Graphical Representations and Results

_{H}stands for the hardware error data from column 7 of Table 1, E

_{S}designates the software error data from column 5 of Table 1, and E

_{I}represents the in-circuit error data from column 6 of Table 1. The above metrics system were calculated for a number of D = 16 flip-flops (for stuck-at-faults), T

_{P}= 840 (for syntax errors), and N

_{R}= 1000 (for structural faults). Accordantly, we obtained the following results, presented in equation system (35) [8]:

_{P}= 840 software test vectors, N

_{R}= 1000 in-circuit routines, according to the equation set (37) [8]:

_{G}= 1 meaning that the solar tracker achieves 100% availability. Additional parameters such as the temperature coefficient and solar irradiance level were substituted according to their STC values. The second test scenario assumed that the mobile PV system was affected by operations errors hindering it from reaching its maximum harvesting potential. We replaced the PR factor with the computed SRF parameters to establish the global energy production of the solar tracking system according to its availability status. To monitor the impact of the SRF, the temperature, as well as the irradiance level, was kept at their STC default values. The global energy production for the mostly sunny week was computed according to equation system (43):

^{2}); the temperature T = 298 K; the solar radiation level is H = 1 kW/m

^{2}; the global reliability factor is SRF

_{G}= 1. The measured voltage and current values were extracted from columns 4 and 5 of Table 2.

_{P}and the measured power gain is only err = 0.03.

^{2}); the temperature T = 298 K; the solar radiation level is H = 1 kW/m

^{2}; the global reliability factor is SRF

_{G}is a variation extracted from equation systems (39) and (41). The measured voltage and current values were extracted from columns 4 and 5 of Table 2 and Table 3. Thus, by substituting all variables with their default values, we obtained the global energy production for the mostly sunny week according to equation system (46):

_{P}is now rated at 4.10 Wh, which is considerably lower than the overall energy consumption of the entire system 6.07 Wh, meaning that the entire setup will require additional energy supplies from the power grid when the solar tracker operates under 73% availability conditions.

_{G}= 1), and 6.01 Wh when the solar tracker is affected by mixed system errors (hardware, software, and in-circuit errors), resulting in a significant power generation reduction of 26.11%. The proposed global energy production equation presents several advantages over state-of-the-art formulas [17,18], as follows: (a) it uses only seven parameters for computing the energy production, holding the average position between work [17] which makes use of 14 parameters, and work [18] that utilizes five parameter values; (b) it is tailored towards static and mobile PV systems, in comparison with works [17,18] which can calculate the energy production only for the static model; (c) it employs novel reliability-oriented metrics that classify robust and durable solar tracking systems according to their performance ratio, showing that fault coverage can significantly impact the solar tracker’s energy production.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Proposed Energy Management Block Diagram for the Solar-Powered Smart Home Automation System.

**Figure 3.**Conceptual Diagram of the Proposed Solar-Powered Smart Home Automation System with integrated Cloud Platform and Testing Facilities.

**Figure 6.**Modified Global Energy Production of the Solar Tracking Device using Reliability-Oriented Metrics for the Mostly Sunny Week.

**Figure 7.**Modified Global Energy Production of the Solar Tracking Device using Reliability-Oriented Metrics for the Partly Cloudy Week.

**Figure 8.**Global Energy Production of the Solar Tracking Device with and without Reliability-Oriented Metrics over Two Weeks.

**Figure 9.**Global Energy Production Computation of the Solar Tracking Device using the SRF Parameter (blue bar) and PR Factor (red bar).

**Table 1.**Experimental results regarding our Hybrid Testing Suite (FPICT + JTAG) [21].

Test Schedule | Fault Coverage (%) | |||||
---|---|---|---|---|---|---|

No. of Days | No. of Test Cases Per Day | Error Types | ||||

8:00 a.m. | 4:00 p.m. | Syntax Errors | Structural Faults | Stuck-at-Faults | ||

Mostly Sunny Week | 1 | 1 | 1 | 70.20 | 78.20 | 60.10 |

2 | 69.10 | 77.10 | 65.10 | |||

3 | 65.14 | 65.20 | 69.15 | |||

4 | 67.20 | 66.20 | 56.10 | |||

5 | 66.66 | 60.20 | 55.45 | |||

6 | 71.13 | 79.90 | 59.10 | |||

7 | 65.20 | 75.13 | 52.01 | |||

Partly Cloudy Week | 1 | 1 | 1 | 69.65 | 77.65 | 62.55 |

2 | 67.67 | 71.70 | 64.62 | |||

3 | 68.70 | 72.70 | 58.10 | |||

4 | 68.43 | 72.20 | 57.27 | |||

5 | 70.66 | 69.20 | 64.12 | |||

6 | 67.70 | 71.65 | 54.10 | |||

7 | 71.16 | 66.20 | 57.30 | |||

Total | 28 | Average Fault Coverage | ||||

67.80 | 71.70 | 59.57 |

**Table 2.**Experimental results regarding Solar Panel Energy Generation and Storage, as well as System Energy Consumption during the Mostly Sunny Week [21].

Time | Solar Panel Output Voltage (V) | Solar Panel Output Current (A) | Accumulator Input Voltage (V) | Accumulator Charging Current (A) | Accumulator Discharging Current (A) | Solar Panel Power Gain (Wh) | System Energy Consumption (Wh) | UV Index |
---|---|---|---|---|---|---|---|---|

Day 1 | 17.33 | 1.2 | 12.5 | 0.88 | 0.45 | 10.97 | 8.21 | 7 |

Day 2 | 17.38 | 1.04 | 12.37 | 0.85 | 0.44 | 10.54 | 6.47 | 7 |

Day 3 | 17.31 | 0.94 | 12.38 | 0.85 | 0.46 | 10.55 | 6.72 | 7 |

Day 4 | 17.41 | 1.04 | 12.45 | 0.86 | 0.5 | 10.77 | 7.29 | 5 |

Day 5 | 16.79 | 0.84 | 12.44 | 0.8 | 0.56 | 9.99 | 8 | 5 |

Day 6 | 17.46 | 1.04 | 12.51 | 0.9 | 0.43 | 11.27 | 6.42 | 6 |

Day 7 | 17.45 | 1.05 | 12.55 | 0.89 | 0.46 | 11.23 | 6.8 | 6 |

Average | 17.3042 | 1.0214 | 12.4571 | 0.8614 | 0.4714 | 10.76 | 7.13 | 6.1428 |

**Table 3.**Experimental results regarding Solar Panel Energy Generation and Storage, as well as System Energy Consumption during the Partly Cloudy Week [21].

Time | Solar Panel Output Voltage (V) | Solar Panel Output Current (A) | Accumulator Input Voltage (V) | Accumulator Charging Current (A) | Accumulator Discharging Current (A) | Solar Panel Power Gain (Wh) | System Energy Consumption (Wh) | UV Index |
---|---|---|---|---|---|---|---|---|

Day 8 | 17.15 | 0.49 | 12.42 | 0.45 | 0.45 | 5.68 | 6.58 | 4 |

Day 9 | 17.04 | 0.47 | 12.41 | 0.38 | 0.36 | 4.8 | 5.58 | 4 |

Day 10 | 17.29 | 0.5 | 12.36 | 0.47 | 0.41 | 5.85 | 6.05 | 3 |

Day 11 | 16.66 | 0.43 | 12.36 | 0.45 | 0.39 | 5 | 5.84 | 3 |

Day 12 | 16.62 | 0.44 | 12.3 | 0.4 | 0.37 | 4.98 | 5.55 | 4 |

Day 13 | 16.8 | 0.5 | 12.39 | 0.48 | 0.37 | 5.98 | 5.61 | 4 |

Day 14 | 17 | 0.54 | 12.37 | 0.51 | 0.5 | 6.34 | 7.28 | 4 |

Average | 16.9371 | 0.4814 | 12.3728 | 0.4485 | 0.4071 | 5.5185 | 6.07 | 3.7142 |

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**MDPI and ACS Style**

Rotar, R.; Jurj, S.L.; Susany, R.; Opritoiu, F.; Vladutiu, M. Global Energy Production Computation of a Solar-Powered Smart Home Automation System Using Reliability-Oriented Metrics. *Energies* **2021**, *14*, 2541.
https://doi.org/10.3390/en14092541

**AMA Style**

Rotar R, Jurj SL, Susany R, Opritoiu F, Vladutiu M. Global Energy Production Computation of a Solar-Powered Smart Home Automation System Using Reliability-Oriented Metrics. *Energies*. 2021; 14(9):2541.
https://doi.org/10.3390/en14092541

**Chicago/Turabian Style**

Rotar, Raul, Sorin Liviu Jurj, Robert Susany, Flavius Opritoiu, and Mircea Vladutiu. 2021. "Global Energy Production Computation of a Solar-Powered Smart Home Automation System Using Reliability-Oriented Metrics" *Energies* 14, no. 9: 2541.
https://doi.org/10.3390/en14092541