# Relationships between the Number and Power of Hungarian Household-Sized Photovoltaic Power Plants and Selected Indicators of the Settlements: A Case Study

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## Abstract

**:**

## 1. Introduction

#### 1.1. Changes in the Spread of Photovoltaic Technology

#### 1.2. The Hungarian System of Feed-in-Tariffs—Overview

#### 1.3. Introduction to the Methodology of Regional Development Indicators in Hungary

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- Every particular settlement indicator is suitable for identifying relationships concerning the number and power of photovoltaic HMKEs.
- -
- The ranking of the settlements based on the complex indicators derived from the settlement indicators shows a correlation with the ranking of the settlements according to the number and power of photovoltaic HMKEs/1000 people in Hungary.
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- A regression model that can be used to determine the quantity of photovoltaic HMKEs in the settlements can be created.

## 2. Materials and Methods

#### 2.1. Methods

_{1}, X

_{2},…, X

_{p}variables can be accounted for by the X

_{j}explanatory variable [38].

_{i j}: normalized basic indicator, min(fa

_{i j}): the lowest value of the basic indicator, max(fa

_{i j}): the highest value of the basic indicator.

_{1}to x

_{n}: independent variables, Y: dependent variable, β

_{1}: the regression coefficient of variable x

_{1}, β

_{n}: the regression coefficient of variable x

_{n}.

#### 2.2. Material

- ELMŰ-ÉMÁSZ, EON (2417 settlements):
- Total HMKE power per 1000 population (kW);
- Residential HMKE power per 1000 population (kW);
- Business HMKE power per 1000 population (kW);
- Total HMKEs per 1000 population (pcs);
- Residential HMKEs per 1000 population (pcs); and
- Business HMKEs per 1000 population (pcs).

- ELMŰ-ÉMÁSZ, EON, NKM (2631 settlements):
- Total HMKEs per 1000 population (pcs).

- EON (1763 settlements):
- Total HMKE power per 1000 population (kW);
- Residential HMKE power per 1000 population (kW);
- Public HMKE power per 1000 population (kW);
- Business HMKE power per 1000 population (kW);
- Total HMKEs per 1000 population (pcs);
- Residential HMKEs per 1000 population (pcs);
- Public HMKEs per 1000 population (pcs); and
- Business HMKEs per 1000 population (pcs).

## 3. Results and Discussions of the Correlation Analysis between the Number and Power of HMKEs and the Settlements’ Level of Development

_{5}: the number of electricity consumers per 1000 population, pcs; x

_{1}: the number of registered economic organizations per 1000 population; x

_{3}: the total budgetary expenditure of municipalities per 1000 population. It was established that both the regression model and the parameters were significant (p = 0.000); furthermore, based on the value of are R (0.554), there was a moderately strong correlation between the explanatory variables of the model and the number of HMKEs per 1000 population, and the five indicators explained the differentiation of the quantity of HMKEs at the settlements’ level to an extent of 30.7%.

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- There are certain settlement indicators each of which individually shows a correlation with the quantity and power of photovoltaic HMKEs. These relationships could be identified irrespective of the service regions of the specific electricity suppliers. There was a moderately strong correlation between the number of registered economic organizations per 1000 population, the number of operating commercial accommodation units per 1000 population, the number of businesses in the hospitality industry per 1000 population, and the quantity of total photovoltaic HMKEs per 1000 population. Furthermore, there was also a moderately strong correlation between the total budgetary expenditure of the municipalities per 1000 population, the number of businesses in the hospitality industry per 1000 population, the number of residential electricity consumers per 1000 population, the number of electricity consumers per 1000 population, the length of the low-voltage electricity distribution network per 1000 population, and the power of total photovoltaic HMKEs per 1000 population.
- -
- The ranking of the settlements based on the complex indicator created from the settlement indicators showed a correlation with the ranking of the settlements according to the number and power of the photovoltaic HMKEs per 1000 population. The relationship between the rankings was moderately strong, with the exception of the residential, public, and business photovoltaic HMKE users in the service region of EON, where the correlation was only weak.
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- A regression model was created from the settlements’ database containing data from all three electricity supplier regions. In this model, the quantity of photovoltaic HMKEs per 1000 population is explained by the number of electricity consumers per 1000 population, the number of economic organizations per 1000 population, and the total budgetary expenditure of the municipalities per 1000 population.

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

ELMŰ-ÉMÁSZ | ELMŰ-ÉMÁSZ Energiaszolgáltató ZRT./ELMŰ-ÉMÁSZ Energy Service Privately Held Company/ |

EON | E.ON Hungária Zrt./E.ON Hungária Privately Held Company/ |

fa_{i j} | normalized basic indicator |

HMKE | household-sized power plants |

CDI | complex development index |

KSH | Központi Statisztikai Hivatal/Hungarian Central Statistical Office/ |

min(fa_{i j}) | the lowest value of the basic indicator |

max(fa_{i j}) | the highest value of the basic indicator |

NKM | NKM Energia Zrt./NKM Energia Privately Held Company/ |

pSi | Polycrystalline |

PV | Photovoltaic |

TEIR | Országos Teületfejlesztési és Területrendezési Információs Rendszer/National Regional Development and Spatial Planning Information System/ |

x_{1} to x_{n} | Represent independent variables |

β_{1} | The regression coefficient of variable x_{1} |

β_{n} | The regression coefficient of variable x_{n}. |

## Appendix A. Correlations between the Quantity and Power of the Residential, Business, and Public HMKEs and the Development Indicators

**Table A1.**The strengths of the relationships between the quantity of the residential HMKEs per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table grey background indicates weak correlations, yellow means weak moderate, and purple moderately close ones. In the case of the partial correlation the blue background signals partial explanation (the controlled variable explains the relationship between variables i and j only partially), while the black one shows irrelevant comparison.).

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | |||||
---|---|---|---|---|---|---|---|

Residential HMKEs per 1000 Population (pcs) | Number of Residential Electricity Consumers per 1000 Population (pcs) | Number of Electricity Consumers per 1000 Population (pcs) | Length of the Low-Voltage Electricity Distribution Network per 1000 Population (km) | Number of Registered Businesses per 1000 Population (pcs) | Number of Units of Operating Commercial Accommodation Units per 1000 Population (pcs) | NUMBER of Businesses in the Hospitality Industry per 1000 Population (pcs) | |

Number of registered businesses per 1000 population (pcs) | 0.272/0.000 | 0.449/0.000 | 0.455/0.000 | 0.395/0.000 | 0.184/0.000 | 0.243/0.000 | |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.099/0.000 | 0.482/0.000 | 0.494/0.000 | 0.439/0.000 | 0.259/0.000 | 0.199/0.000 | 0.262/0.000 |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.140/0.000 | 0.476/0.000 | 0.487/0.000 | 0.432/0.000 | 0.249/0.000 | 0.192/0.000 | 0.256/0.000 |

Number of residential electricity consumers per 1000 population (pcs) | 0.490/0.000 | 0.129/0.000 | 0.150/0.000 | 0.166/0.000 | 0.053/0.000 | 0.049/0.000 | |

Number of electricity consumers per 1000 population (pcs) | 0.502/0.000 | 0.024/0.000 | 0.132/0.000 | 0.136/0.000 | 0.050/0.000 | 0.054/0.000 | |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.448/0.000 | 0.264/0.000 | 0.283/0.000 | 0.148/0.000 | 0.114/0.000 | 0.170/0.000 | |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.213/0.000 | 0.454/0.000 | 0.467/0.000 | 0.417/0.000 | 0.251/0.000 | 0.189/0.000 | |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.276/0.000 | 0.423/0.000 | 0.439/0.000 | 0.400/0.000 | 0.239/0.000 | 0.061/0.000 |

**Table A2.**The strengths of the relationships between the power of the residential HMKEs per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table grey background indicates weak correlations, yellow means weak moderate, and purple moderately close ones. In the case of the partial correlation the blue background signals partial explanation (the controlled variable explains the relationship between variables i and j only partially), while the black one shows irrelevant comparison.).

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | ||||
---|---|---|---|---|---|---|

Residential HMKE Power per 1000 Population (kW) | Number of Residential Electricity Consumers per 1000 Population (pcs) | Number of Electricity Consumers per 1000 Population (pcs) | Length of the Low-Voltage Electricity Distribution Network per 1000 Population (km) | Number of Registered Businesses per 1000 Population (pcs) | Number of Businesses in the Hospitality Industry per 1000 Population (pcs) | |

Number of registered businesses per 1000 population (pcs) | 0.274/0.000 | 0.362/0.000 | 0.385/0.000 | 0.347/0.000 | 0.183/0.000 | |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.078/0.000 | 0.403/0.000 | 0.433/0.000 | 0.399/0.000 | 0.265/0.000 | 0.207/0.000 |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.115/0.000 | 0.397/0.000 | 0.426/0.000 | 0.392/0.000 | 0.256/0.000 | 0.201/0.000 |

Number of residential electricity consumers per 1000 population (pcs) | 0.409/0.000 | 0.183/0.000 | 0.169/0.000 | 0.185/0.000 | 0.024/0.000 | |

Number of electricity consumers per 1000 population (pcs) | 0.439/0.000 | –0.059/0.000 | 0.133/0.000 | 0.157/0.000 | 0.017/0.000 | |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.405/0.000 | 0.180/0.000 | 0.226/0.000 | 0.163/0.000 | 0.115/0.000 | |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.163/0.000 | 0.381/0.000 | 0.413/0.000 | 0.381/0.000 | 0.257/0.000 | 0.153/0.000 |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.219/0.000 | 0.395/0.000 | 0.390/0.000 | 0.366/0.000 | 0.247/0.000 |

**Table A3.**The strengths of the relationships between the quantity of the business HMKEs per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table grey background indicates weak correlations and yellow means weak moderate ones. In the case of the partial correlation the red background indicates a partially distorted correlation, blue signals partial explanation (the controlled variable explains the relationship between variables i and j only partially), while the black one shows irrelevant comparison.).

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | ||||
---|---|---|---|---|---|---|

Business HMKEs per 1000 Population (pcs) | Number of Registered Businesses per 1000 Population (pcs) | Total Budgetary Revenue of the Municipalities per 1000 Population (HUF 1000) | Total Budgetary Expenditure of the Municipalities per 1000 Population (HUF 1000) | Number of Electricity Consumers per 1000 Population (pcs) | Length of the Low-Voltage Electricity Distribution Network per 1000 Population (km) | |

Number of registered businesses per 1000 population (pcs) | 0.305/0.000 | 0.250/0.000 | 0.303/0.000 | 0.145/0.000 | 0.113/0.000 | |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.291/0.000 | 0.266/0.000 | 0.205/0.000 | 0.181/0.000 | 0.151/0.000 | |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.351/0.000 | 0.246/0.000 | 0.011/0.000 | 0.151/0.000 | 0.131/0.000 | |

Number of residential electricity consumers per 1000 population (pcs) | 0.178/0.000 | 0.271/0.000 | 0.269/0.000 | 0.327/0.000 | 0.188/0.000 | 0.106/0.000 |

Number of electricity consumers per 1000 population (pcs) | 0.229/0.000 | 0.251/0.000 | 0.256/0.000 | 0.310/0.000 | 0.050/0.000 | |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.202/0.000 | 0.258/0.000 | 0.260/0.000 | 0.319/0.000 | 0.121/0.000 | |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.144/0.000 | 0.291/0.000 | 0.271/0.000 | 0.333/0.000 | 0.193/0.000 | 0.172/0.000 |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.047/0.020 | 0.302/0.000 | 0.288/0.000 | 0.349/0.000 | 0.234/0.000 | 0.197/0.000 |

**Table A4.**The strengths of the relationships between the power of the business HMKEs per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table grey background indicates weak correlations and yellow means weak moderate ones. In the case of the partial correlation the blue background signals partial explanation (the controlled variable explains the relationship between variables i and j only partially), while the black one shows irrelevant comparison.).

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | ||
---|---|---|---|---|

Business HMKE Power per 1000 Population (kW) | Number of Registered Businesses per 1000 Population (pcs) | Total Budgetary Revenue of the Municipalities per 1000 Population (HUF 1000) | Total Budgetary Expenditure of the Municipalities per 1000 Population (HUF 1000) | |

Number of registered businesses per 1000 population (pcs) | 0.241/0.000 | 0.177/0.000 | 0.210/0.000 | |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.214/0.000 | 0.209/0.000 | 0.140/0.000 | |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.254/0.000 | 0.194/0.000 | 0.014/0.000 | |

Number of residential electricity consumers per 1000 population (pcs) | 0.138/0.000 | 0.213/0.000 | 0.195/0.000 | 0.233/0.000 |

Number of electricity consumers per 1000 population (pcs) | 0.156/0.000 | 0.204/0.000 | 0.188/0.000 | 0.223/0.000 |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.151/0.000 | 0.205/0.000 | 0.189/0.000 | 0.227/0.000 |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.107/0.000 | 0.230/0.000 | 0.198/0.000 | 0.239/0.000 |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.098/0.000 | 0.229/0.000 | 0.198/0.000 | 0.241/0.000 |

**Table A5.**The strengths of the relationships between the quantity of the residential HMKEs per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table grey background indicates weak correlations, yellow means weak moderate, and purple moderately close ones. In the case of the partial correlation the blue background signals partial explanation (the controlled variable explains the relationship between variables i and j only partially), while the black one shows irrelevant comparison.).

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | |||||
---|---|---|---|---|---|---|---|

Residential HMKEs per 1000 Population (pcs) | Number of Residential Electricity Consumers per 1000 Population (pcs) | Number of Electricity Consumers per 1000 Population (pcs) | Number of Registered Businesses per 1000 Population (pcs) | Length of the Low-Voltage Electricity Distribution Network per 1000 Population (km) | Number of Units of Operating Commercial Accommodation Units per 1000 Population (pcs) | Number of Businesses in the Hospitality Industry per 1000 Population (pcs) | |

Number of registered businesses per 1000 population (pcs) | 0.212/0.000 | 0.479/0.000 | 0.481/0.000 | 0.346/0.000 | 0.174/0.000 | 0.273/0.000 | |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.060/0.012 | 0.503/0.000 | 0.507/0.000 | 0.205/0.000 | 0.383/0.000 | 0.196/0.000 | 0.293/0.000 |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.064/0.007 | 0.502/0.000 | 0.506/0.000 | 0.204/0.000 | 0.382/0.000 | 0.195/0.000 | 0.292/0.000 |

Number of residential electricity consumers per 1000 population (pcs) | 0.505/0.000 | 0.074/0.000 | 0.111/0.000 | 0.029/0.000 | 0.016/0.000 | 0.051/0.000 | |

Number of electricity consumers per 1000 population (pcs) | 0.508/0.000 | –0.028/0.000 | 0.098/0.000 | 0.014/0.000 | 0.006/0.000 | 0.044/0.000 | |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.387/0.000 | 0.353/0.000 | 0.358/0.000 | 0.106/0.000 | 0.121/0.000 | 0.207/0.000 | |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.204/0.000 | 0.472/0.000 | 0.476/0.000 | 0.184/0.000 | 0.355/0.000 | 0.225/0.000 |

**Table A6.**The strengths of the relationships between the power of the residential HMKEs per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table grey background indicates weak correlations, yellow means weak moderate, and purple moderately close ones. In the case of the partial correlation the blue background signals partial explanation (the controlled variable explains the relationship between variables i and j only partially), while the black one shows irrelevant comparison.).

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | ||||
---|---|---|---|---|---|---|

Residential HMKE Power per 1000 Population (kW) | Number of Residential Electricity Consumers per 1000 Population (pcs) | Number of Electricity Consumers per 1000 Population (pcs) | Number of Registered Businesses per 1000 Population (pcs) | Length of the Low-Voltage Electricity Distribution Network per 1000 Population (km) | Number of Businesses in the Hospitality Industry per 1000 Population (pcs) | |

Number of registered businesses per 1000 population (pcs) | 0.210/0.000 | 0.381/0.000 | 0.384/0.000 | 0.315/0.000 | 0.205/0.000 | |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.054/0.024 | 0.409/0.000 | 0.415/0.000 | 0.204/0.000 | 0.354/0.000 | 0.227/0.000 |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.059/0.014 | 0.408/0.000 | 0.414/0.000 | 0.203/0.000 | 0.354/0.000 | 0.226/0.000 |

Number of residential electricity consumers per 1000 population (pcs) | 0.411/0.000 | 0.099/0.000 | 0.127/0.000 | 0.091/0.000 | 0.027/0.000 | |

Number of electricity consumers per 1000 population (pcs) | 0.417/0.000 | –0.064/0.008 | 0.117/0.000 | 0.077/0.000 | 0.019/0.000 | |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.358/0.000 | 0.235/0.000 | 0.242/0.000 | 0.112/0.000 | 0.141/0.000 | |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.155/0.000 | 0.386/0.000 | 0.392/0.000 | 0.188/0.000 | 0.334/0.000 | 0.177/0.000 |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.233/0.000 | 0.349/0.000 | 0.356/0.000 | 0.178/0.000 | 0.310/0.000 |

**Table A7.**The strengths of the relationships between the quantity of the public HMKEs per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table white background indicates non–significant relationships, grey weak and yellow means weak moderate ones. In the case of the partial correlation the red background indicates a partially distorted correlation and blue signals partial explanation (the controlled variable explains the relationship between variables i and j only partially), while black shows irrelevant comparison.).

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | |
---|---|---|---|

Public HMKEs per 1000 Population (pcs) | Total Budgetary Revenue of the Municipalities per 1000 Population (HUF 1000) | Total Budgetary Expenditure of the Municipalities per 1000 Population (HUF 1000) | |

Number of registered businesses per 1000 population (pcs) | 0.055/0.020 | 0.215/0.000 | 0.256/0.000 |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.221/0.000 | 0.145/0.000 | |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.262/0.000 | 0.016/0.000 | |

Number of residential electricity consumers per 1000 population (pcs) | –0.001/0.981 | 0.224/0.000 | 0.265/0.000 |

Number of electricity consumers per 1000 population (pcs) | 0.010/0.661 | 0.223/0.000 | 0.264/0.000 |

Length of the low-voltage electricity distribution network per 1000 population (km) | –0.005/0.845 | 0.225/0.000 | 0.266/0.000 |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.008/0.742 | 0.223/0.000 | 0.266/0.000 |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.006/0.791 | 0.227/0.000 | 0.268/0.000 |

**Table A8.**The strengths of the relationships between the power of the public HMKEs per 1000 population and the settlement indicatorsv

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table white background indicates non–significant relationships, grey weak and yellow means weak moderate ones. In the case of the partial correlation the green background signals cases where the background variable did not cause any difference in the strength of the relationship, while blue background indicates partial explanation (the controlled variable explains the relationship between variables i and j only partially), and black marking shows irrelevant comparison.).

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | |
---|---|---|---|

Public HMKE Power per 1000 Population (kW) | Total Budgetary Revenue of the Municipalities per 1000 Population (HUF 1000) | Total Budgetary Expenditure of the Municipalities per 1000 Population (HUF 1000) | |

Number of registered businesses per 1000 population (pcs) | 0.052/0.028 | 0.219/0.000 | 0.240/0.000 |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.225/0.000 | 0.110/0.000 | |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.245/0.000 | 0.047/0.000 | |

Number of residential electricity consumers per 1000 population (pcs) | 0.045/0.057 | 0.221/0.000 | 0.241/0.000 |

Number of electricity consumers per 1000 population (pcs) | 0.051/0.031 | 0.219/0.000 | 0.240/0.000 |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.019/0.417 | 0.225/0.000 | 0.245/0.000 |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.053/0.025 | 0.219/0.000 | 0.239/0.000 |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.125/0.000 | 0.201/0.000 | 0.223/0.000 |

**Table A9.**The strengths of the relationships between the quantity of the business HMKEs per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table grey background indicates weak correlations and yellow means weak moderate ones. In the case of the partial correlation the blue background signals partial explanation (the controlled variable explains the relationship between variables i and j only partially), while the black one shows irrelevant comparison.).

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | |
---|---|---|---|

Business HMKEs per 1000 Population (pcs) | Number of Registered Businesses per 1000 Population (pcs) | Number of Units of Operating Commercial Accommodation Units per 1000 Population (pcs) | |

Number of registered businesses per 1000 population (pcs) | 0.254/0.000 | 0.178/0.000 | |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.129/0.000 | 0.237/0.000 | 0.193/0.000 |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.185/0.000 | 0.226/0.000 | 0.180/0.000 |

Number of residential electricity consumers per 1000 population (pcs) | 0.157/0.000 | 0.226/0.000 | 0.168/0.000 |

Number of electricity consumers per 1000 population (pcs) | 0.182/0.000 | 0.218/0.000 | 0.157/0.000 |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.174/0.000 | 0.214/0.000 | 0.178/0.000 |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.213/0.000 | 0.226/0.000 | |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.076/0.001 | 0.245/0.000 | 0.211/0.000 |

**Table A10.**The strengths of the relationships between the power of the business HMKEs per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table grey background indicates weak correlations.

Indicator | Business HMKE Power per 1000 Population (kW) |
---|---|

Number of registered businesses per 1000 population (pcs) | 0.130/0.000 |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.113/0.000 |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.138/0.000 |

Number of residential electricity consumers per 1000 population (pcs) | 0.063/0.008 |

Number of electricity consumers per 1000 population (pcs) | 0.076/0.001 |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.068/0.004 |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.082/0.001 |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.055/0.022 |

## References

- International Renewable Energy Agency (IRENA). Global Energy Transformation: A Roadmap to 2050; IRENA: Abu Dhabi, UAE, 2018. [Google Scholar]
- Renewable Energy Policy Network for the 21st Century (REN21). Renewables 2018 Global Status Report—REN21; REN21: Paris, France, 2018. [Google Scholar]
- International Energy Agency (IEA). World Energy Outlook 2017; IEA: Paris, France, 2017. [Google Scholar]
- Kim, K.J.; Lee, H.; Koo, Y. Research on local acceptance cost of renewable energy in South Korea: A case study of photovoltaic and wind power projects. Energy Policy
**2020**, 144, 111684. [Google Scholar] [CrossRef] - Dominković, D.F.; Bačeković, I.; Sveinbjörnsson, D.; Pedersen, A.S.; Krajačić, G. On the way towards smart energy supply in cities: The impact of interconnecting geographically distributed district heating grids on the energy system. Energy
**2017**, 137, 941–960. [Google Scholar] [CrossRef] [Green Version] - Kordmahaleh, A.A.; Naghashzadegan, M.; Javaherdeh, K.; Khoshgoftar, M. Design of a 25 MWe Solar Thermal Power Plant in Iran with Using Parabolic Trough Collectors and a Two-Tank Molten Salt Storage System. Int. J. Photoenergy
**2017**, 2017, 4210184. [Google Scholar] [CrossRef] [Green Version] - Noman, A.M.; Addoweesh, K.E.; Alolah, A.I. Simulation and Practical Implementation of ANFIS-Based MPPT Method for PV Applications Using Isolated Ćuk Converter. Int. J. Photoenergy
**2017**, 2017, 1–15. [Google Scholar] [CrossRef] [Green Version] - Daliento, S.; Chouder, A.; Guerriero, P.; Pavan, A.M.; Mellit, A.; Moeini, R.; Tricoli, P. Monitoring, Diagnosis, and Power Forecasting for Photovoltaic Fields: A Review. Int. J. Photoenergy
**2017**, 2017, 1–13. [Google Scholar] [CrossRef] - Sefa, İ.; Demirtas, M.; Çolak, İ. Application of one-axis sun tracking system. Energy Convers. Manag.
**2009**, 50, 2709–2718. [Google Scholar] [CrossRef] - Nengroo, S.; Kamran, M.; Ali, M.; Kim, D.-H.; Kim, M.-S.; Hussain, A.; Kim, H.; Nengroo, S.H.; Kamran, M.A.; Ali, M.U.; et al. Dual Battery Storage System: An Optimized Strategy for the Utilization of Renewable Photovoltaic Energy in the United Kingdom. Electronics
**2018**, 7, 177. [Google Scholar] [CrossRef] [Green Version] - Turner, J.A. A realizable renewable energy future. Science
**1999**, 285, 687–689. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Lin, A.; Lu, M.; Sun, P.; Lin, A.; Lu, M.; Sun, P. The Influence of Local Environmental, Economic and Social Variables on the Spatial Distribution of Photovoltaic Applications across China’s Urban Areas. Energies
**2018**, 11, 1986. [Google Scholar] [CrossRef] [Green Version] - Liu, Z.; Wu, D.; Yu, H.; Ma, W.; Jin, G. Field measurement and numerical simulation of combined solar heating operation modes for domestic buildings based on the Qinghai–Tibetan plateau case. Energy Build.
**2018**, 167, 312–321. [Google Scholar] [CrossRef] - Alsafasfeh, M.; Abdel-Qader, I.; Bazuin, B.; Alsafasfeh, Q.; Su, W.; Alsafasfeh, M.; Abdel-Qader, I.; Bazuin, B.; Alsafasfeh, Q.; Su, W. Unsupervised Fault Detection and Analysis for Large Photovoltaic Systems Using Drones and Machine Vision. Energies
**2018**, 11, 2252. [Google Scholar] [CrossRef] [Green Version] - Hosenuzzaman, M.; Rahim, N.A.; Selvaraj, J.; Hasanuzzaman, M.; Malek, A.B.M.A.; Nahar, A. Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation. Renew. Sustain. Energy Rev.
**2015**, 41, 284–297. [Google Scholar] [CrossRef] - Roth, W. General Concepts of Photovoltaic Power Supply Systems; Fraunhofer Institute for Solar Energy Systems ISE: Freiburg, Germany, 2005; pp. 1–23. [Google Scholar]
- Kumar Sahu, B. A study on global solar PV energy developments and policies with special focus on the top ten solar PV power producing countries. Renew. Sustain. Energy Rev.
**2015**, 43, 621–634. [Google Scholar] [CrossRef] - Renewable Energy Policy Network for the 21st Century. Renewables 2020 Global Status Report—REN21; REN21: Paris, France, 2020. [Google Scholar]
- Solargis.com. Solar Resource Maps and GIS Data for 200+ Countries. Available online: https://solargis.com/maps-and-gis-data/overview (accessed on 28 October 2020).
- PV Magazine. Hungary to See Record PV Growth in 2018. Available online: https://www.pv-magazine.com/2018/09/12/hungary-to-see-record-pv-growth-in-2018/ (accessed on 28 October 2020).
- Hungarian Transmission System Operator—MAVIR ZRt. Renewable Support System—Current Information. Available online: https://www.mavir.hu/web/mavir/aktualis-informaciok (accessed on 28 October 2020).
- Fülöp, M. Működik az első Hazai Közcélú Energiatároló Egység—The First Domestic Public Energy Storage Unit Is Operating. Available online: https://www.villanylap.hu/hirek/4904-mukodik-az-elso-hazai-kozcelu-energiatarolo-egyseg (accessed on 28 October 2020).
- Igazságügyi Minisztérium. Magyar Közlöny, 2019. évi 222. Szám; Igazságügyi Minisztérium: Budapest, Hungary, 2019. [Google Scholar]
- Magyar Villamosenergia-Ipari Átviteli Rendszerirányító Zártkörűen Működő Részvénytársaság (MAVIR ZRt.). A Magyar VER Fogyasztói és Forrásoldali Jövőképe 2020–2040—Input Adatok; MAVIR ZRt.: Budapest, Hungary, 2020. [Google Scholar]
- Fraunhofer Institute for Solar Energy Systems. Photovoltaics Report; Fraunhofer Institute for Solar Energy Systems: Freiburg, Germany, 2018. [Google Scholar]
- Hungarian Energy and Public Utility Regulatory Authority. Renewable Energy Operating Aid. Available online: https://www.enhat.mekh.hu/mukodesi-tamogatas (accessed on 25 August 2020).
- Hungarian Energy and Public Utility Regulatory Authority. Report—On Quarterly New Household-Sized Power Plants (Q4 2019); Hungarian Energy and Public Utility Regulatory Authority: Budapest, Hungary, 2019. [Google Scholar]
- Wolters Kluwer Hungary Kft. 84/1993. (XI. 11.) OGY Decision. Available online: https://mkogy.jogtar.hu/jogszabaly?docid=993h0084.OGY (accessed on 17 September 2020).
- Wolters Kluwer Hungary Kft. 290/2014. (XI. 26.) Government Decree. Available online: https://net.jogtar.hu/jogszabaly?docid=a1400290.kor (accessed on 17 September 2020).
- Wolters Kluwer Hungary Kft. 105/2015. (IV. 23.) Government Decree. Available online: https://net.jogtar.hu/jogszabaly?docid=a1500105.kor (accessed on 17 September 2020).
- Kovács, P.; Bodnár, G. Examining the Factors of Endogenous Development in Hungarian Rural Areas by Means of PLS Path Analysis. Reg. Stat.
**2017**, 7, 90–114. [Google Scholar] [CrossRef] - Valkó, G.; Fekete-Farkas, M.; Kovács, I. Indicators for the economic dimension of sustainable agriculture in the European Union. Reg. Stat.
**2017**, 7, 179–196. [Google Scholar] [CrossRef] - Hungarian Central Statistical Office (KSH). Information Database, Regional Statistics. Available online: http://statinfo.ksh.hu/Statinfo/themeSelector.jsp?lang=hu (accessed on 17 September 2020).
- Land Information System (TEIR). Application Supporting the Planning of LEADER Local Development Strategies. Available online: https://www.teir.hu/leader/ (accessed on 17 September 2020).
- Foster, G.C.; Lane, D.; Scott, D.; Hebl, M.; Guerra, R. An Introduction to Psychological Statistics; University of Missouri-St. Louis: St. Louis, MO, USA, 2018. [Google Scholar]
- Illowsky, B.; Dean, S. Introductory Statistics; 12th Media Services: Suwanee, GA, USA, 2017. [Google Scholar]
- Freedman, D.; Pisani, R.; Purves, R. Statistics, 4th ed.; W. W. Norton & Company: New York, NY, USA, 2017. [Google Scholar]
- Zaid, M.A. Correlation and Regression Analysis; The Statistical, Economic and Social Research and Training Centre for Islamic Countries (SESRIC): Ankara, Turkey, 2015. [Google Scholar]
- Montgomery, C.D.; Peck, A.E.; Vining, G.G. Introduction to Linear Regression Analysis, 5th ed.; Wiley: Hoboken, NJ, USA, 2012. [Google Scholar]
- Pintér, G.; Zsiborács, H.; Hegedűsné Baranyai, N.; Vincze, A.; Birkner, Z. The Economic and Geographical Aspects of the Status of Small-Scale Photovoltaic Systems in Hungary—A Case Study. Energies
**2020**, 13, 3489. [Google Scholar] [CrossRef] - Lechner Nonprofit Kft. Map of Utilities. Available online: https://www.e-epites.hu/e-kozmu (accessed on 17 September 2020).

**Figure 1.**Europe’s photovoltaic power potential [19].

**Figure 2.**The nominal power of the PV HMKE systems in Hungary [27].

**Figure 3.**The regions of the Hungarian electric companies based on [41].

Indicators | Symbol as Explanatory Variable in the Model |
---|---|

number of registered enterprises per 1000 population | x_{1} |

total revenue of the municipalities per 1000 population (HUF 1000) | x_{2} |

total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | x_{3} |

residential electricity consumers per 1000 population | x_{4} |

electricity consumers per 1000 population | x_{5} |

length of the low-voltage electricity distribution network per 1000 population (km) | x_{6} |

number of operating commercial accommodation units per 1000 population | x_{7} |

number of businesses in the hospitality industry per 1000 population | x_{8} |

**Table 2.**The strength of the relationship between the quantity of the total HMKEs per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table grey background indicates weak correlations, yellow means weak moderate, and purple moderately close ones. In the case of the partial correlation the blue background signals partial explanation (the controlled variable explains the relationship between variables i and j only partially), while the black one shows irrelevant comparison.

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | |||||
---|---|---|---|---|---|---|---|

HMKEs per 1000 Population (pcs) ELMŰ-ÉMÁSZ, EON, NKM | Number of Residential Electricity Consumers per 1000 Population (pcs) | Number of Electricity Consumers per 1000 Population (pcs) | Number of Registered Businesses per 1000 Population (pcs) | Total Budgetary Expenditure of the Municipalities per 1000 Population (HUF 1000) | Number of Units of Operating Commercial Accommodation Units per 1000 Population (pcs) | Number of Businesses in the Hospitality Industry per 1000 Population (pcs) | |

Number of registered businesses per 1000 population (pcs) | 0.305/0.000 | 0.438/0.000 | 0.468/0.000 | 0.228/0.000 | 0.221/0.000 | 0.210/0.000 | |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.123/0.000 | 0.461/0.000 | 0.494/0.000 | 0.280/0.000 | 0.180/0.000 | 0.214/0.000 | 0.207/0.000 |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.275/0.000 | 0.451/0.000 | 0.478/0.000 | 0.263/0.000 | 0.205/0.000 | 0.202/0.000 | |

Number of residential electricity consumers per 1000 population (pcs) | 0.480/0.000 | 0.227/0.000 | 0.217/0.000 | 0.208/0.000 | 0.096/0.000 | 0.013/0.511 | |

Number of electricity consumers per 1000 population (pcs) | 0.515/0.000 | –0.077/0.000 | 0.188/0.000 | 0.172/0.000 | 0.085/0.000 | 0.004/0.828 | |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.035/0.000 | 0.356/0.000 | 0.405/0.000 | 0.213/0.000 | 0.225/0.000 | 0.188/0.000 | 0.174/0.000 |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.246/0.000 | 0.434/0.000 | 0.473/0.000 | 0.286/0.000 | 0.241/0.000 | 0.123/0.000 | |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.243/0.000 | 0.427/0.000 | 0.469/0.000 | 0.280/0.000 | 0.241/0.000 | 0.129/0.000 |

**Table 3.**The strength of the relationship between the quantity of the total HMKE per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table yellow background means weak moderate and purple moderately close correlations.) In the case of the partial correlation the green background signals cases where the background variable did not cause any difference in the strength of the relationship, while blue background indicates partial explanation (the controlled variable explains the relationship between variables i and j only partially), and black marking shows irrelevant comparison.

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | |||||||
---|---|---|---|---|---|---|---|---|---|

Total HMKEs per 1000 Population (pcs) | Number of Registered Businesses per 1000 Population (pcs) | Total Budgetary Revenue of the Municipalities per 1000 Population (HUF 1000) | Total Budgetary Expenditure of the Municipalities per 1000 Population (HUF 1000) | Number of Residential Electricity Consumers per 1000 Population (pcs) | Number of Electricity Consumers per 1000 Population (pcs) | Length of the Low-Voltage Electricity Distribution Network per 1000 Population (km) | Number of Units of Operating Commercial Accommodation Units per 1000 Population (pcs) | Number of Businesses in the Hospitality Industry per 1000 Population (pcs) | |

Number of registered businesses per 1000 population (pcs) | 0.365/0.000 | 0.160/0.000 | 0.212/0.000 | 0.436/0.000 | 0.460/0.000 | 0.393/0.000 | 0.209/0.000 | 0.208/0.000 | |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.215/0.000 | 0.339/0.000 | 0.179/0.000 | 0.470/0.000 | 0.501/0.000 | 0.441/0.000 | 0.211/0.000 | 0.215/0.000 | |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.276/0.000 | 0.323/0.000 | –0.017/0.000 | 0.460/0.000 | 0.485/0.000 | 0.429/0.000 | 0.220/0.000 | 0.210/0.000 | |

Number of residential electricity consumers per 1000 population (pcs) | 0.489/0.000 | 0.277/0.000 | 0.152/0.000 | 0.207/0.000 | 0.220/0.000 | 0.179/0.000 | 0.243/0.000 | 0.017/0.000 | |

Number of electricity consumers per 1000 population (pcs) | 0.523/0.000 | 0.245/0.000 | 0.129/0.000 | 0.169/0.000 | –0.066/0.000 | 0.137/0.000 | 0.154/0.000 | 0.010/0.000 | |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.466/0.000 | 0.254/0.000 | 0.137/0.000 | 0.194/0.000 | 0.245/0.000 | 0.299/0.000 | 0.240/0.000 | 0.138/0.000 | |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.243/0.000 | 0.345/0.000 | 0.176/0.000 | 0.242/0.000 | 0.445/0.000 | 0.482/0.000 | 0.430/0.000 | 0.135/0.000 | |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.252/0.000 | 0.339/0.000 | 0.169/0.000 | 0.239/0.000 | 0.434/0.000 | 0.473/0.000 | 0.424/0.000 | 0.119/0.000 |

**Table 4.**The strengths of the relationships between the power of the total HMKE per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table grey background indicates weak correlations and yellow means weak moderate ones.) In the case of the partial correlation the blue background signals partial explanation (the controlled variable explains the relationship between variables i and j only partially), while the black one shows irrelevant comparison.

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | ||||||
---|---|---|---|---|---|---|---|---|

Power of Total HMKEs per 1000 Population (kW) | Number of Registered Businesses per 1000 Population (pcs) | Total Budgetary Revenue of the Municipalities per 1000 Population (HUF 1000) | Total Budgetary Expenditure of the Municipalities per 1000 Population (HUF 1000) | Number of Residential Electricity Consumers per 1000 Population (pcs) | Number of Electricity Consumers per 1000 Population (pcs) | Length of the Low-Voltage Electricity Distribution Network per 1000 Population (km) | Number of Businesses in the Hospitality Industry per 1000 Population (pcs) | |

Number of registered businesses per 1000 population (pcs) | 0.350/0.000 | 0.149/0.000 | 0.191/0.000 | 0.301/0.000 | 0.323/0.000 | 0.291/0.000 | 0.168/0.000 | |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.203/0.000 | 0.324/0.000 | 0.159/0.000 | 0.344/0.000 | 0.372/0.000 | 0.345/0.000 | 0.176/0.000 | |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.255/0.000 | 0.309/0.000 | –0.008/0.688 | 0.332/0.000 | 0.355/0.000 | 0.333/0.000 | 0.172/0.000 | |

Number of residential electricity consumers per 1000 population (pcs) | 0.366/0.000 | 0.280/0.000 | 0.152/0.000 | 0.198/0.000 | 0.184/0.000 | 0.164/0.000 | 0.042/0.000 | |

Number of electricity consumers per 1000 population (pcs) | 0.398/0.000 | 0.256/0.000 | 0.135/0.000 | 0.170/0.000 | –0.075/0.000 | 0.126/0.000 | 0.033/0.000 | |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.373/0.000 | 0.260/0.000 | 0.138/0.000 | 0.185/0.000 | 0.147/0.000 | 0.196/0.000 | 0.118/0.000 | |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.183/0.000 | 0.334/0.000 | 0.173/0.000 | 0.228/0.000 | 0.328/0.000 | 0.364/0.000 | 0.343/0.000 | 0.131/0.000 |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.213/0.000 | 0.327/0.000 | 0.164/0.000 | 0.223/0.000 | 0.308/0.000 | 0.346/0.000 | 0.333/0.000 |

**Table 5.**The strength of the relationship between the quantity of the total HMKEs per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (In the case of the Pearson correlation coefficients in the table grey background indicates weak correlations, yellow means weak moderate, and purple moderately close ones. In the case of the partial correlation the blue background signals partial explanation (the controlled variable explains the relationship between variables i and j only partially), while the black one shows irrelevant comparison.

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | |||||
---|---|---|---|---|---|---|---|

Total HMKEs per 1000 Population (pcs) | Number of Residential Electricity Consumers per 1000 Population (pcs) | Number of Electricity Consumers per 1000 Population (pcs) | Number of Registered Businesses per 1000 Population (pcs) | Length of the Low-Voltage Electricity Distribution Network per 1000 Population (km) | Number of Units of Operating Commercial Accommodation Units per 1000 Population (pcs) | Number of Businesses in the Hospitality Industry per 1000 Population (pcs) | |

Number of registered businesses per 1000 population (pcs) | 0.222/0.000 | 0.378/0.000 | 0.382/0.000 | 0.194/0.000 | 0.197/0.000 | 0.320/0.000 | |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.158/0.000 | 0.395/0.000 | 0.400/0.000 | 0.201/0.000 | 0.226/0.000 | 0.203/0.000 | 0.319/0.000 |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.169/0.000 | 0.394/0.000 | 0.398/0.000 | 0.196/0.000 | 0.226/0.000 | 0.198/0.000 | 0.318/0.000 |

Number of residential electricity consumers per 1000 population (pcs) | 0.411/0.000 | 0.109/0.000 | 0.142/0.000 | –0.085/0.000 | 0.086/0.000 | 0.171/0.000 | |

Number of electricity consumers per 1000 population (pcs) | 0.417/0.000 | –0.074/0.002 | 0.131/0.000 | –0.104/0.000 | 0.077/0.000 | 0.163/0.000 | |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.248/0.000 | 0.347/0.000 | 0.360/0.000 | 0.159/0.000 | 0.177/0.000 | 0.293/0.000 | |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.227/0.000 | 0.360/0.000 | 0.366/0.000 | 0.191/0.000 | 0.203/0.000 | 0.268/0.000 | |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.345/0.000 | 0.289/0.000 | 0.296/.000 | 0.178/0.000 | 0.161/0.000 | 0.024/0.000 |

**Table 6.**The strengths of the relationships between the power of the total HMKE per 1000 population and the settlement indicators

^{1}(

^{1}Pearson correlation coefficient/p-value; if p < 0.05, then there is a significant verified relationship between the two variables) (in the case of the Pearson correlation coefficients in the table grey background indicates weak correlations and yellow means weak moderate ones.) In the case of the partial correlation the blue background signals partial explanation (the controlled variable explains the relationship between variables i and j only partially), while the black one shows irrelevant comparison.

Indicator | Pearson Correlation Coefficient | Partial Correlation Coefficient | |||||
---|---|---|---|---|---|---|---|

Power of toTal HMKEs per 1000 Population (kW) | Number of Registered Businesses per 1000 Population (pcs) | Total Budgetary Expenditure of the Municipalities per 1000 Population (HUF 1000) | Number of Residential Electricity Consumers per 1000 Population (pcs) | Number of Electricity Consumers per 1000 Population (pcs) | Length of the Low-Voltage Electricity Distribution Network per 1000 Population (km) | Number of Businesses in the Hospitality Industry per 1000 Population (pcs) | |

Number of registered businesses per 1000 population (pcs) | 0.246/0.000 | 0.173/0.000 | 0.301/0.000 | 0.313/0.000 | 0.242/0.000 | 0.208/0.000 | |

Total budgetary revenue of the municipalities per 1000 population (HUF 1000) | 0.186/0.000 | 0.221/0.000 | 0.107/0.000 | 0.321/0.000 | 0.334/0.000 | 0.275/0.000 | 0.203/0.000 |

Total budgetary expenditure of the municipalities per 1000 population (HUF 1000) | 0.212/0.000 | 0.214/0.000 | 0.318/0.000 | 0.330/0.000 | 0.273/0.000 | 0.200/0.000 | |

Number of residential electricity consumers per 1000 population (pcs) | 0.342/0.000 | 0.181/0.000 | 0.168/0.000 | 0.210/0.000 | 0.076/0.000 | 0.079/0.000 | |

Number of electricity consumers per 1000 population (pcs) | 0.356/0.000 | 0.171/0.000 | 0.159/0.000 | –0.182/0.000 | 0.053/0.000 | 0.067/0.000 | |

Length of the low-voltage electricity distribution network per 1000 population (km) | 0.299/0.000 | 0.170/0.000 | 0.172/0.000 | 0.189/0.000 | 0.210/0.000 | 0.165/0.000 | |

Number of units of operating commercial accommodation units per 1000 population (pcs) | 0.178/0.000 | 0.222/0.000 | 0.181/0.000 | 0.302/0.000 | 0.317/0.000 | 0.267/0.000 | 0.169/0.000 |

Number of businesses in the hospitality industry per 1000 population (pcs) | 0.240/0.000 | 0.215/0.000 | 0.164/0.000 | 0.262/0.000 | 0.279/0.000 | 0.245/0.000 |

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**MDPI and ACS Style**

Baranyai, N.H.; Zsiborács, H.; Vincze, A.; Rodek, N.; Makai, M.; Pintér, G.
Relationships between the Number and Power of Hungarian Household-Sized Photovoltaic Power Plants and Selected Indicators of the Settlements: A Case Study. *Processes* **2021**, *9*, 4.
https://doi.org/10.3390/pr9010004

**AMA Style**

Baranyai NH, Zsiborács H, Vincze A, Rodek N, Makai M, Pintér G.
Relationships between the Number and Power of Hungarian Household-Sized Photovoltaic Power Plants and Selected Indicators of the Settlements: A Case Study. *Processes*. 2021; 9(1):4.
https://doi.org/10.3390/pr9010004

**Chicago/Turabian Style**

Baranyai, Nóra Hegedűsné, Henrik Zsiborács, András Vincze, Nóra Rodek, Martina Makai, and Gábor Pintér.
2021. "Relationships between the Number and Power of Hungarian Household-Sized Photovoltaic Power Plants and Selected Indicators of the Settlements: A Case Study" *Processes* 9, no. 1: 4.
https://doi.org/10.3390/pr9010004