scholarly journals Statistical Investigation of Climate Change Effects on the Utilization of the Sediment Heat Energy

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 435
Author(s):  
Nebiyu Girgibo ◽  
Anne Mäkiranta ◽  
Xiaoshu Lü ◽  
Erkki Hiltunen
Keyword(s):  
Climate Change ◽  
Moving Average ◽  
Arima Model ◽  
Measurement Data ◽  
Statistical Investigation ◽  
Heat Energy ◽  
Land Uplift ◽  
Seabed Sediment ◽  
Climate Change Effects ◽  
Sediment Temperature

Suvilahti, a suburb of the city of Vaasa in western Finland, was the first area to use seabed sediment heat as the main source of heating for a high number of houses. Moreover, in the same area, a unique land uplift effect is ongoing. The aim of this paper is to solve the challenges and find opportunities caused by global warming by utilizing seabed sediment energy as a renewable heat source. Measurement data of water and air temperature were analyzed, and correlations were established for the sediment temperature data using Statistical Analysis System (SAS) Enterprise Guide 7.1. software. The analysis and provisional forecast based on the autoregression integrated moving average (ARIMA) model revealed that air and water temperatures show incremental increases through time, and that sediment temperature has positive correlations with water temperature with a 2-month lag. Therefore, sediment heat energy is also expected to increase in the future. Factor analysis validations show that the data have a normal cluster and no particular outliers. This study concludes that sediment heat energy can be considered in prominent renewable production, transforming climate change into a useful solution, at least in summertime.

Temporal Data Analysis and Mining Methods for Modelling the Climate Change Effects on Malaysia's Oil Palm Yield at Different Regional Scales

2017 ◽  
pp. 499-531
Author(s):  
Subana Shanmuganathan ◽  
Ajit Narayanan ◽  
Nishantha Priyanka Kumara Medagoda
Keyword(s):  
Climate Change ◽  
Oil Palm ◽  
Moving Average ◽  
Weather Data ◽  
Crop Models ◽  
Related Data ◽  
Climate Change Effects ◽  
Moving Average Models ◽  
Recent Trends ◽  
Temporal Data Analysis

Space and time related data generated is becoming ever more voluminous, noisy and heterogeneous outpacing the research efforts in the domain of climate. Nevertheless, this data portrays recent climate/ weather change patterns. Thus, insightful approaches are required to overcome the challenges when handling the so called “big data” to unravel the recent unprecedented climate change in particular, its variability, frequency and effects on key crops. Contemporary climate-crop models developed at least two decades ago are found to be unsuitable for analysing complex climate/weather data retrospectively. In this context, the chapter looks at the use of scalable time series analysis, namely ARIMA (Autoregressive integrated moving average) models and data mining techniques to extract new knowledge on the climate change effects on Malaysia's oil palm yield at the regional and administrative divisional scales. The results reveal recent trends and patterns in climate change and its effects on oil palm yield impossible otherwise e.g. Traditional statistical methods alone.

scholarly journals Multi-Horizon Air Pollution Forecasting with Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1235
Author(s):  
Mirche Arsov ◽  
Eftim Zdravevski ◽  
Petre Lameski ◽  
Roberto Corizzo ◽  
Nikola Koteli ◽  
...  
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Short Term Memory ◽  
Moving Average ◽  
Arima Model ◽  
Measurement Data ◽  
Quality Measurement ◽  
Capital City ◽  
Industrial Plants ◽  
Proactive Measures

Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures.

Detection of regional climate change effects on alpine hydrology by daily resolution trend analysis in Tyrol, Austria

2014 ◽  
Vol 6 (1) ◽  
pp. 124-143 ◽  
Author(s):  
Christoph Kormann ◽  
Till Francke ◽  
Axel Bronstert
Keyword(s):  
Climate Change ◽  
Temporal Dynamics ◽  
Moving Average ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Early Summer ◽  
Climate Change Effects ◽  
Investigation Area ◽  
Time Periods ◽  
Mean Climate

Owing to average temperature increases of at least twice the global mean, climate change is expected to have strong impacts on local hydrology and climatology in the Alps. Nevertheless, trend analyses of hydro-climatic station data rarely reveal clear patterns concerning climate change signals except in temperature observations. However, trend research has thus far mostly been based on analysing trends of averaged data such as yearly, seasonal or monthly averages and has therefore often not been able to detect the finer temporal dynamics. For this reason, we derived 30-day moving average trends, providing a daily resolution of the timing and magnitude of trends within the seasons. Results are validated by including different time periods. We studied daily observations of mean temperature, liquid and solid precipitation, snow height and runoff in the relatively dry central Alpine region in Tyrol, Austria. Our results indicate that the vast majority of changes are observed throughout spring to early summer, most likely triggered by the strong temperature increase during this season. Temperature, streamflow and snow trends have clearly amplified during recent decades. The overall results are consistent over the entire investigation area and different time periods.

Temporal Data Analysis and Mining Methods for Modelling the Climate Change Effects on Malaysia's Oil Palm Yield at Different Regional Scales

2016 ◽  
pp. 482-513
Author(s):  
Subana Shanmuganathan ◽  
Ajit Narayanan ◽  
Nishantha Priyanka Kumara Medagoda
Keyword(s):  
Climate Change ◽  
Oil Palm ◽  
Moving Average ◽  
Weather Data ◽  
Crop Models ◽  
Related Data ◽  
Climate Change Effects ◽  
Moving Average Models ◽  
Recent Trends ◽  
Temporal Data Analysis

Space and time related data generated is becoming ever more voluminous, noisy and heterogeneous outpacing the research efforts in the domain of climate. Nevertheless, this data portrays recent climate/ weather change patterns. Thus, insightful approaches are required to overcome the challenges when handling the so called “big data” to unravel the recent unprecedented climate change in particular, its variability, frequency and effects on key crops. Contemporary climate-crop models developed at least two decades ago are found to be unsuitable for analysing complex climate/weather data retrospectively. In this context, the chapter looks at the use of scalable time series analysis, namely ARIMA (Autoregressive integrated moving average) models and data mining techniques to extract new knowledge on the climate change effects on Malaysia's oil palm yield at the regional and administrative divisional scales. The results reveal recent trends and patterns in climate change and its effects on oil palm yield impossible otherwise e.g. Traditional statistical methods alone.

scholarly journals RESEARCH ON THE COMBUSTION PROCESS USING TIME SERIES

2020 ◽  
Vol 10 (2) ◽  
pp. 52-55
Author(s):  
Żaklin Grądz
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Combustion Process ◽  
Measurement Data ◽  
Stationary Time Series ◽  
Autoregressive Integrated Moving Average ◽  
One Dimensional ◽  
And Control ◽  
Stationary Form

In the combustion process, one of the most important tasks is related to maintaining its stability. Numerous methods of monitoring, diagnostics, and analysis of the measurement data are used for this purpose. The information recorded in the combustion chamber constitute one-dimensional time series. In the case of non-stationary time series, which can be transformed into the stationary form, the autoregressive integrated moving average process can be employed. The paper presented the issue of forecasting the changes in flame luminosity. The investigations discussed in the work were carried out with the ARIMA model (p,d,q). The presented forecasts of changes in flame luminosity reflect the actual processes, which enables to employ them in diagnostics and control of the combustion process.

Assessment of aquifers groundwater storage for the mitigation of climate change effects

2016 ◽  
Vol 39 ◽  
pp. 89-92 ◽  
Author(s):  
Luca Alberti ◽  
Martino Cantone ◽  
Loris Colombo ◽  
Gabriele Oberto ◽  
Ivana La Licata
Keyword(s):  
Climate Change ◽  
Groundwater Storage ◽  
Climate Change Effects

Model Statistika Prediksi Energi Surya Dengan Menggunakan Autoregresif Integrated Moving Average (ARIMA)

Jurnal MIPA ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 181
Author(s):  
Imriani Moroki ◽  
Alfrets Septy Wauran
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Moving Average ◽  
Arima Model ◽  
Solar Irradiation ◽  
Energy Sources ◽  
Energy Potential ◽  
Good Design ◽  
Potential Sources ◽  
The Common

Energi terbarukan adalah salah satu masalah energi paling terkenal saat ini. Ada beberapa sumber potensial energi terbarukan. Salah satu energi terbarukan yang umum dan sederhana adalah energi matahari. Masalah besar ketersediaan energi saat ini adalah terbatasnya sumber energi konvensional seperti bahan bakar. Ini semua sumber energi memiliki banyak masalah karena memiliki jumlah energi yang terbatas. Penting untuk membuat model dan analisis berdasarkan ketersediaan sumber energi. Energi matahari adalah energi terbarukan yang paling disukai di negara-negara khatulistiwa saat ini. Tergantung pada produksi energi surya di daerah tertentu untuk memiliki desain dan analisis energi matahari yang baik. Untuk memiliki analisis yang baik tentang itu, dalam makalah ini kami membuat model prediksi energi surya berdasarkan data iradiasi matahari. Kami membuat model energi surya dan angin dengan menggunakan Metode Autoregresif Integrated Moving Average (ARIMA). Model ini diimplementasikan oleh R Studio yang kuat dari statistik. Sebagai hasil akhir, kami mendapatkan model statistik solar yang dibandingkan dengan data aktualRenewable energy is one of the most fomous issues of energy today. There are some renewable energy potential sources. One of the common n simple renewable energy is solar energy. The big problem of the availability of energy today is the limeted sources of conventional enery like fuel. This all energy sources have a lot of problem because it has a limited number of energy. It is important to make a model and analysis based on the availability of the energy sources. Solar energy is the most prefered renewable energy in equator countries today. It depends on the production of solar energy in certain area to have a good design and analysis of  the solar energy. To have a good analysis of it, in this paper we make a prediction model of solar energy based on the data of solar irradiation. We make the solar and wind enery model by using Autoregresif Integrated Moving Average (ARIMA) Method. This model is implemented by R Studio that is a powerfull of statistical. As the final result, we got the statistical model of solar comparing with the actual data

Estimating Climate Change Effects on Installation Energy Use

2012 ◽  
Author(s):  
Ronald Filadelfo ◽  
Jonathon Mintz ◽  
Daniel Carvell ◽  
Alan Marcus
Keyword(s):  
Climate Change ◽  
Energy Use ◽  
Climate Change Effects
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