scholarly journals Forecasting approach for solar power based on weather parameters (Case study: East Kalimantan)

2021 ◽  
Vol 2106 (1) ◽  
pp. 012022
Author(s):  
P Hasanah ◽  
S A Wiradinata ◽  
M Azka
Keyword(s):  
Linear Regression ◽  
Solar Energy ◽  
Multiple Linear Regression ◽  
Solar Power ◽  
Weather Data ◽  
Climate Data ◽  
Energy Forecasting ◽  
Tropical Country ◽  
East Kalimantan ◽  
Weather Parameters

Abstract Solar Energy is the most popular among several clean energies. As a tropical country, Indonesia has big opportunity to develop solar power, particularly in East Kalimantan which spans around the equator. Solar energy generation, however, is influenced by weather parameters which give uncertain values of the amount of the captured energy. Therefore, this research is conducted to overcome the effect of weather towards solar energy. The aim of this research is to examine the model for sun power forecasting based on the data. The Artificial Neural Network (ANN) and Multiple Linear Regression have taken as the approach models to determine energy forecasting. This study used five input variables; temperature, precipitation level, humidity, wind speed, and surface pressure, while the solar radiation was taken as the output variable. Moreover, the daily solar power and weather data from East Kalimantan has been taken along the period of 27th July 2018 – 28th July 2021. The result of this study showed that the RMSE of ANN was slightly similar with the multiple linear regression methods which were calculated by 160.26 and 160.46 respectively. However, the ANN is preferable to use in the solar energy forecasting since the tendency of nonlinearity of the climate data.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

scholarly journals Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wissanupong Kliengchuay ◽  
Rachodbun Srimanus ◽  
Wechapraan Srimanus ◽  
Sarima Niampradit ◽  
Nopadol Preecha ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Local Level ◽  
Winter Season ◽  
Summer Season ◽  
Weather Data ◽  
Good Prediction ◽  
Mlr Model

Abstract Background The northern regions of Thailand have been facing haze episodes and transboundary air pollution every year in which particulate matter, particularly PM10, accumulates in the air, detrimentally affecting human health. Chiang Rai province is one of the country’s most popular tourist destinations as well as an important economic hub. This study aims to develop and compare the best-fitted model for PM10 prediction for different seasons using meteorological factors. Method The air pollution and weather data acquired from the Pollution Control Department (PCD) spanned from the years 2011 until 2018 at two stations on an hourly basis. Four different stepwise Multiple Linear Regression (MLR) models for predicting the PM10 concentration were then developed, namely annual, summer, rainy, and winter seasons. Results The maximum daily PM10 concentration was observed in the summer season for both stations. The minimum daily concentration was detected in the rainy season. The seasonal variation of PM10 was significantly different for both stations. CO was moderately related to PM10 in the summer season. The PM10 summer model was the best MLR model to predict PM10 during haze episodes. In both stations, it revealed an R2 of 0.73 and 0.61 in stations 65 and 71, respectively. Relative humidity and atmospheric pressure display negative relationships, although temperature is positively correlated with PM10 concentrations in summer and rainy seasons. Whereas pressure plays a positive relationship with PM10 in the winter season. Conclusions In conclusion, the MLR models are effective at estimating PM10 concentrations at the local level for each seasonal. The annual MLR model at both stations indicates a good prediction with an R2 of 0.61 and 0.52 for stations 65 and 73, respectively.

Modeling of TEC over the Iberian Peninsula using PCA decomposition and multiple linear regression on space weather parameters

2021 ◽  
Author(s):  
Anna Morozova ◽  
Tatiana Barlyaeva ◽  
Teresa Barata
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Iberian Peninsula ◽  
Space Weather ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Electron Content ◽  
Time Lags ◽  
Total Electron ◽  
Weather Parameters

<p>The total electron content (TEC) over the Iberian Peninsula was modeled using a three-step procedure. At the 1<sup>st</sup> step the TEC series is decomposed using the principal component analysis (PCA) into several daily modes. Then, the amplitudes of those daily modes is fitted by a multiple linear regression model (MRM) using several types of space weather parameters as regressors. Finally, the TEC series is reconstructed using the PCA daily modes and MRM fitted amplitudes.</p><p>The advantage of such approach is that seasonal variations of the TEC daily modes are automatically extracted by PCA. As space weather parameters we considered proxies for the solar UV and XR fluxes, number of the solar flares, parameters of the solar wind and the interplanetary magnetic field, and geomagnetic indices. Different time lags and combinations of the regressors are tested.</p><p>The possibility to use such TEC models for forecasting was tested. Also, a possibility to use neural networks (NN) instead of MRM is studied.</p>

Future Scope of Solar Energy in India

2016 ◽  
Vol 8 (01) ◽  
pp. 20-25 ◽  
Author(s):  
Bharat Raj Singh ◽  
Onkar Singh
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Rural Areas ◽  
Solar Power ◽  
Geographical Location ◽  
Energy Generation ◽  
Green Energy ◽  
Tropical Country ◽  
Lighting Systems ◽  
Almost All

Generation of solar energy has tremendous scope in India. The geographical location of the country stands to its benefit for generating solar energy. The reason being India is a tropical country and it receives solar radiation almost throughout the year, which amounts to 3,000 hours of sunshine. This is equal to more than 5,000 trillion kWh. Almost, all parts of India receive 4-7 kWh of solar radiation per sq metres. This is equivalent to 2,300–3,200 sunshine hours per year. States like Andhra Pradesh, Bihar, Gujarat, Haryana, Madhya Pradesh, Maharashtra, Orissa, Punjab, Rajasthan, and West Bengal have great potential for tapping solar energy due to their location. Since majority of the population live in rural areas, there is much scope for solar energy being promoted in these areas. Use of solar energy can reduce the use of firewood and dung cakes by rural household. Many large projects have been proposed in India, some of them are: i).Thar Desert of India has best solar power projects, estimated to generate 700 to 2,100 GW, ii). The Jawaharlal Nehru National Solar Mission (JNNSM) launched by the Centre is targeting 20,000 MW of solar energy power by 2022, iii).Gujarat’s pioneering solar power policy aims at 1,000 MW of solar energy generation, and Rs. 130 billion solar power plan was unveiled in July 2009, which projected to produce 20 GW of solar power by 2020. Apart from above, about 66 MW is installed for various applications in the rural area, amounting to be used in solar lanterns, street lighting systems and solar water pumps, etc. Thus, India has massive plan for Solar Energy generation that may not only fulfill the deficit of power generation but also contribute largely in Green Energy Production to help to reduce the Climatic Changes globally.

scholarly journals Pengaruh Suhu, Hujan dan Kelembaban Terhadap Kerusakan Jalan di Kabupaten Pacitan

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Diana Rendrarini
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Secondary Data ◽  
Multiple Linear Regression Analysis ◽  
Tropical Country ◽  
Ex Post ◽  
Stepwise Method

Indonesia as a tropical country has two seasons, namely the dry season and the rainy season. Each season changes usually have a significant influence in several regions. Pacitan Regency is an area in East Java, part of which is the coast and mountains. Several roads in Pacitan Regency have damaged conditions, including waterlogging, potholes and bumpy conditions. Some factors that cause damaged road conditions include the influence of temperature, rain, and humidity. In this study data analysis was performed using Multiple Linear Regression analysis with the Stepwise method. Where this research is quantitative and ex-post facto research. The data collection method uses the documentation method, which is collecting secondary data from several related agencies. Based on the results of data analysis using Multiple Linear Regression Analysis with the Stepwise method, it can be concluded that the temperature factor has an influence on road damage, while the rain and humidity factors do not have any effect.

scholarly journals The Impact of Understanding, Religiousity, and Faith towards the Intention to Pay Professional Zakat by Muzakki at Lembaga Amil Zakat Inisiatif Zakat Indonesia (LAZ-IZI) Balikpapan City, East Kalimantan

2018 ◽  
pp. 23
Author(s):  
Ana Mulyana ◽  
Sri Wahyuni
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Positive Impact ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Significance Level ◽  
East Kalimantan ◽  
Level Of Significance ◽  
The Impact

The dynamics of Muslim society affects the development of the discourse regarding Islamic sharia, including that of professional zakat, which is now enforced in the province of East Kalimantan, where many Muslim work as professionals. This research explored the impact of understanding, religiousity, and faith towards the intention to pay professional zakat by muzakki. The research was quantitative using the formula of multiple linear regression analysis. The data were collected through questionnaires from the sample of 94 muzakki, taken using Slovin formula, out of the population of 1,516 professional muzakki at Lembaga Amil Zakat Inisiatif Zakat Indonesia (LAZ-IZI) Balikpapan City, East Kalimantan. The findings revealed that understanding had a positive impact but not significant on the intention to pay professional zakat as indicated with t_(calculation ) 1,577 <t_tabel1,986 with the level of significance = 0,118 higher than α = 0,05; religiosity has a positive impact and significant on the intention to pay professional zakat as indicated with t_calculation 2,724>t_tabel1,986 with significance level = 0,008 lower than α = 0,05; and faith has a positive impact and significant on the intention to pay professional zakat as shown with t_(calculation ) 2,376>t_tabel=1,986 with significance level = 0,020 lower than α = 0,05. In sum, understanding has a positive but not significant impact on the intention to pay professional zakat, and both religiosity and faith have a positive and significant impact on the intention to pay professional zakat among professional muzakki in Balikpapan, East Kalimantan.

scholarly journals Statistical Analysis of Weather Parameters for Sustainable Flight Operation in Nigeria

2021 ◽  
Vol 14 (3-4) ◽  
pp. 47-53
Author(s):  
Abiodun Daniel Olabode
Keyword(s):  
Wind Speed ◽  
Cloud Cover ◽  
Climate Models ◽  
Regional Climate ◽  
Linear Regression Analysis ◽  
Weather Data ◽  
Climate Data ◽  
Weather System ◽  
Study Region ◽  
Weather Parameters

Abstract The recent complications in the weather system, which oftentimes lead to flight cancellation, delay and diversion have become a critical issue in Nigeria. This study however considers the weather related parameters and their impacts on flight disruption in the country. Weather data (on thunderstorm, wind speed and direction, visibility and cloud cover) and flight data (delay, cancellation and diversion) were collected from Murtala International Airport, Ikeja-Lagos, Nigeria. The data covered the period between 2005 and 2020. However, Regional Climate Models (RCMs) were also used to run climate data projections between year 2020 and 2035 in the study region. The study employed Statistical Package for Social Sciences (SPSS) software for the descriptive and inferential analysis. Time series analysis, Pearson Moment Correlation for interrelationship among the weather parameters and the flight disruption data, and multiple linear regression analysis were applied to determine the influence of weather parameters on flight disruption data. Results show that cloud cover and high visibility are negatively correlated. Wind speed has positive relationship with wind direction; and an inverse relationship between visibility, thunderstorm, and fog. Direct relationship exists between highest visibility and thick dust, wind speed and cloud cover. Thick dust, wind speed and cloud cover indicate increased visibility level in the study area. Flight delay is prominent over flight diversion and cancellation, which indicates their relevance in air traffic of the study area. The prediction model indicates high degree of cloud cover at the beginning of every year and later declines sharply in 2035, the visibility flattens out by the year 2025, and low pattern of thick dust was calculated in the same pattern in 2011, 2016 and 2027. Based on this conclusion, the study recommends accurate weather reporting and strict compliance to safety regulations, and attention should be paid to changing pattern of weather parameters in order to minimize fight related disasters.

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