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1 – 10 of 871In this paper, an emerging state-of-the-art machine intelligence technique called the Hierarchical Temporal Memory (HTM) is applied to the task of short-term load forecasting…
Abstract
In this paper, an emerging state-of-the-art machine intelligence technique called the Hierarchical Temporal Memory (HTM) is applied to the task of short-term load forecasting (STLF). A HTM Spatial Pooler (HTM-SP) stage is used to continually form sparse distributed representations (SDRs) from a univariate load time series data, a temporal aggregator is used to transform the SDRs into a sequential bivariate representation space and an overlap classifier makes temporal classifications from the bivariate SDRs through time. The comparative performance of HTM on several daily electrical load time series data including the Eunite competition dataset and the Polish power system dataset from 2002 to 2004 are presented. The robustness performance of HTM is also further validated using hourly load data from three more recent electricity markets. The results obtained from experimenting with the Eunite and Polish dataset indicated that HTM will perform better than the existing techniques reported in the literature. In general, the robustness test also shows that the error distribution performance of the proposed HTM technique is positively skewed for most of the years considered and with kurtosis values mostly lower than a base value of 3 indicating a reasonable level of outlier rejections.
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Patrik Jonsson, Johan Öhlin, Hafez Shurrab, Johan Bystedt, Azam Sheikh Muhammad and Vilhelm Verendel
This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?
Abstract
Purpose
This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?
Design/methodology/approach
A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.
Findings
The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.
Practical implications
The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.
Originality/value
The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?
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Vicente Ramos, Woraphon Yamaka, Bartomeu Alorda and Songsak Sriboonchitta
This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a…
Abstract
Purpose
This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a novel high-frequency forecasting methodology applied on big data characterized by fine-grained time and spatial resolution; Second, this paper elaborates on those estimates’ usefulness for visitors and tourism public and private stakeholders, whose decisions are increasingly focusing on short-time horizons.
Design/methodology/approach
This study uses the technical communications between mobile devices and WiFi networks to build a high frequency and precise geolocation of big data. The empirical section compares the forecasting accuracy of several artificial intelligence and time series models.
Findings
The results robustly indicate the long short-term memory networks model superiority, both for in-sample and out-of-sample forecasting. Hence, the proposed methodology provides estimates which are remarkably better than making short-time decision considering the current number of residents and visitors (Naïve I model).
Practical implications
A discussion section exemplifies how high-frequency forecasts can be incorporated into tourism information and management tools to improve visitors’ experience and tourism stakeholders’ decision-making. Particularly, the paper details its applicability to managing overtourism and Covid-19 mitigating measures.
Originality/value
High-frequency forecast is new in tourism studies and the discussion sheds light on the relevance of this time horizon for dealing with some current tourism challenges. For many tourism-related issues, what to do next is not anymore what to do tomorrow or the next week.
Plain Language Summary
This research initiates high-frequency forecasting in tourism and hospitality studies. Additionally, we detail several examples of how anticipating urban crowdedness requires high-frequency data and can improve visitors’ experience and public and private decision-making.
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Xiaojie Xu and Yun Zhang
Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present…
Abstract
Purpose
Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.
Design/methodology/approach
The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.
Findings
The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.
Originality/value
Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.
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Ian Seymour Yeoman and Una McMahon-Beattie
The primary aim of revenue management (RM) is to sell the right product to the right customer at the right time for the right price. Ever since the deregulation of US airline…
Abstract
Purpose
The primary aim of revenue management (RM) is to sell the right product to the right customer at the right time for the right price. Ever since the deregulation of US airline industry, and the emergence of the internet as a distribution channel, RM has come of age. The purpose of this paper is to map out ten turning points in the evolution of Revenue Management taking an historical perspective.
Design/methodology/approach
The paper is a chronological account based upon published research and literature fundamentally drawn from the Journal of Revenue and Pricing Management.
Findings
The significance and success to RM is attributed to the following turning points: Littlewood’s rule, Expected Marginal Seat Revenue, deregulation of the US air industry, single leg to origin and destination RM, the use of family fares, technological advancement, low-cost carriers, dynamic pricing, consumer and price transparency and pricing capabilities in organizations.
Originality/value
The originality of the paper lies in identifying the core trends or turning points that have shaped the development of RM thus assisting futurists or forecasters to shape the future.
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Matteo Podrecca and Marco Sartor
The aim of this paper is to present the first diffusion analysis of ISO/IEC 27001, the fourth most popular ISO certification at global level and the most important standard for…
Abstract
Purpose
The aim of this paper is to present the first diffusion analysis of ISO/IEC 27001, the fourth most popular ISO certification at global level and the most important standard for information security.
Design/methodology/approach
To achieve the purposes, the authors applied Grey Models (GM) – Even GM (1,1), Even GM (1,1,α,θ), Discrete GM (1,1), Discrete GM (1,1,α) – complemented by the relative growth rate and the doubling time indexes on the six most important countries in terms of issued certificates.
Findings
Results show that a growing trend is likely to be expected in the years to come and that China will lead at country level.
Originality/value
The study contributes to the scientific debate by presenting the first diffusive analysis of ISO/IEC 27001 and by proposing a forecasting approach that to date has found little application in the field of international standards.
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Matevz Obrecht, Rhythm Singh and Timitej Zorman
This paper aims to forecast the availability of used but operational electric vehicle (EV) batteries to integrate them into a circular economy concept of EVs' end-of-life (EOL…
Abstract
Purpose
This paper aims to forecast the availability of used but operational electric vehicle (EV) batteries to integrate them into a circular economy concept of EVs' end-of-life (EOL) phase. Since EVs currently on the roads will become obsolete after 2030, this study focuses on the 2030–2040 period and links future renewable electricity production with the potential for storing it into used EVs' batteries. Even though battery capacity decreases by 80% or less, these batteries will remain operational and can still be seen as a valuable solution for storing peaks of renewable energy production beyond EV EOL.
Design/methodology/approach
Storing renewable electricity is gaining as much attention as increasing its production and share. However, storing it in new batteries can be expensive as well as material and energy-intensive; therefore, existing capacities should be considered. The use of battery electric vehicles (BEVs) is among the most exciting concepts on how to achieve it. Since reduced battery capacity decreases car manufacturers' interest in battery reuse and recycling is environmentally hazardous, these batteries should be integrated into the future electricity storage system. Extending the life cycle of batteries from EVs beyond the EV's life cycle is identified as a potential solution for both BEVEOL and electricity storage.
Findings
Results revealed a rise of photovoltaic (PV) solar power plants and an increasing number of EVs EOL that will have to be considered. It was forecasted that 6.27–7.22% of electricity from PV systems in scenario A (if EV lifetime is predicted to be 20 years) and 18.82–21.68% of electricity from PV systems in scenario B (if EV lifetime is predicted to be 20 years) could be stored in batteries. Storing electricity in EV batteries beyond EV EOL would significantly decrease the need for raw materials, increase energy system and EV sustainability performance simultaneously and enable leaner and more efficient electricity production and distribution network.
Practical implications
Storing electricity in used batteries would significantly decrease the need for primary materials as well as optimizing lean and efficient electricity production network.
Originality/value
Energy storage is one of the priorities of energy companies but can be expensive as well as material and energy-intensive. The use of BEV is among the most interesting concepts on how to achieve it, but they are considered only when in the use phase as vehicle to grid (V2G) concept. Because reduced battery capacity decreases the interest of car manufacturers to reuse batteries and recycling is environmentally risky, these batteries should be used for storing, especially renewable electricity peaks. Extending the life cycle of batteries beyond the EV's life cycle is identified as a potential solution for both BEV EOL and energy system sustainability, enabling more efficient energy management performance. The idea itself along with forecasting its potential is the main novelty of this paper.
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Fouad Ben Abdelaziz, Herb Kunze, Davide La Torre and Bernard Sinclair-Desgagné