Data Set Analysis of Electric Power Consumption

Беляева, Н В; Петроченков, А. Б.; Bade, Korinna

doi:10.13187/er.2013.61.2482

Cited by 7 publications

(6 citation statements)

References 3 publications

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“…One of the most distinctive features of the time series is that data is not generated independently; their dispersion varies in time, is often governed by a trend, and has cyclic components. An observed time series can be decomposed into three components: the trend (long term direction), the seasonal (systematic, calendar-related movements), and the irregular (unsystematic, short term fluctuations) (Beliaeva et al, 2013).…”

Section: Time-series Datamentioning

confidence: 99%

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Recovery of Slovenian Tourism After Covid-19 and Ukraine Crisis

Ljubotina

Raspor

2022

Economics

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The profound impact of the COVID-19 pandemic and the Ukraine crisis 2022 on tourism has rendered forecasts of tourism demand obsolete. Accordingly, scholars have begun to look for the best methods to predict the recovery of tourism. In this study, econometric and judgmental methods were combined in order to to forecast the possible ways to tourism recovery in Slovenia. The goals of this paper are to show and assess the current offerings and potential for the development of tourism in Slovenia, and to point out the advantages and challenges in the development of tourism in this type of destination. Statistical methods were used for benchmark analysis, while Box and Jenkins approach and ARIMA modeling were used for forecasting Slovenian tourism until 2030. The datasets are analyzed using Tableau 2022.1. Our findings show that Slovenian tourism will be slower on the uptake and is not likely to fully recover until 2026, due to COVID-19 related uncertainty. A steep jump in daily COVID-19 infections in China and the Ukraine crisis which is affecting political and economic stability in the world is also slowing down the speed of recovery. In 2019, Chinese, Russian and Ukrainian tourists generated 5% of all tourist arrivals and overnight stays. In particular Russian tourists were well-known for their high consumer expenditure and thus desirable to the destination places. To mitigate the drop in tourist arrivals, Slovenia should do two things, namely concentrate on traditional markets, i.e. Austria, Italy, and Germany, which altogether represent 1/3 of all tourist arrivals and overnight stays, and secondly, it should target new markets, bringing new, innovative products, services, and experiences.

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Section: Time-series Datamentioning

confidence: 99%

“…The ARIMA (autoregressive integrated moving average) modeling can be applied to most types of time series data. The forecasting accuracy of the ARIMA model is considered by scientists to be of a high degree (Beliaeva et al, 2013).…”

Section: Choosing and Fitting The Modelmentioning

confidence: 99%

Recovery of Slovenian Tourism After Covid-19 and Ukraine Crisis

Ljubotina

Raspor

2022

Economics

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Section: Choosing and Fitting The Modelmentioning

confidence: 99%

Impact of Terrorism on Tourism Demand in the EU and the Mediterranean

Berce¹,

Lacmanović²,

Raspor³

2021

6th International Thematic Monograph: Modern Management Tools and Economy of Tourism Sector in Present Era

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The impact of terrorism on tourism causes the specific context in which tourism trips are realised. The relationship between terrorism and tourism was researched many decades ago. There are many open questions regarding the stated impact. The main goal of this paper is to find out some important facts considering the impact of terrorism regarding global and regional tourist demand. The article emphasises that security is an important factor for stable tourism growth. Security is certainly one of the most important factors for a tourist when deciding for a destination. However, this is all more important in the countries where the political and economic situation is not stable. There are still doubts what is the reason for a greater negative effect on tourism in the countries that are not considered western. Is it only about perception and media coverage or are non-western countries negating the effects with better responses and PR activities? Using methodology applied in this research, country statistical offices can estimate when tourism will return to its pre-terrorist period.

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“…A specialized subset of machine learning is coined as “deep learning,” which [ 51 – 58 ] overcomes the accuracy issues [ 59 ]. Deep learning architectures are composed of nonlinear computation at multiple levels, such as neural nets performed by many hidden layers [ 60 ] with more abstraction and complexity.…”

Section: Introduction To Deep Learning Recurrent Neural Network Theorymentioning

confidence: 99%

“…A specialized subset of machine learning is coined as "deep learning," which [51][52][53][54][55][56][57][58] overcomes the accuracy issues [59].…”

Section: Introduction To Deep Learning Recurrent Neural Network Theorymentioning

confidence: 99%

Deep Learning Neural Network Prediction System Enhanced with Best Window Size in Sliding Window Algorithm for Predicting Domestic Power Consumption in a Residential Building

Tomar

Bhardwaj

et al. 2022

Computational Intelligence and Neuroscience

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Buildings are considered to be one of the world’s largest consumers of energy. The productive utilization of energy will spare the accessible energy assets for the following ages. In this paper, we analyze and predict the domestic electric power consumption of a single residential building, implementing deep learning approach (LSTM and CNN). In these models, a novel feature is proposed, the “best N window size” that will focus on identifying the reliable time period in the past data, which yields an optimal prediction model for domestic energy consumption known as deep learning recurrent neural network prediction system with improved sliding window algorithm. The proposed prediction system is tuned to achieve high accuracy based on various hyperparameters. This work performs a comparative study of different variations of the deep learning model and records the best Root Mean Square Error value compared to other learning models for the benchmark energy consumption dataset.

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Data Set Analysis of Electric Power Consumption

Cited by 7 publications

References 3 publications

Recovery of Slovenian Tourism After Covid-19 and Ukraine Crisis

Recovery of Slovenian Tourism After Covid-19 and Ukraine Crisis

Impact of Terrorism on Tourism Demand in the EU and the Mediterranean

Deep Learning Neural Network Prediction System Enhanced with Best Window Size in Sliding Window Algorithm for Predicting Domestic Power Consumption in a Residential Building

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