Alternative techniques for forecasting mineral commodity prices

Forecasting mineral commodity(MC) prices has been an important and difficult task traditionally addressed by econometric, stochastic-Gaussian and time series techniques. None of these techniques has proved suitable to represent the dynamic behavior and time related nature of MC markets. Chaos theory(CT) and machine learning(ML) techniques are able to represent the temporal relationships of variables and their evolution has been used separately to better understand and represent MC markets. CT can determine a system's dynamics in the form of time delay and embedding dimension. However, this information has often been solely used to describe the system's behavior and not for forecasting.Compared to traditional techniques, ML has better performance for forecasting MC prices, due to its capacity for finding patterns governing the system's dynamics. However, the rational nature of economic problems increases concerns regarding the use of hidden patterns for forecasting. Therefore, it is uncertain if variables selected and hidden patterns found by ML can represent the economic rationality.Despite their refined features for representing system dynamics, the separate use of either CT or ML does not provide the expected realistic accuracy. By itself, neither CT nor ML are able to identify the main variables affecting systems, recognize the relation and influence of variables though time, and discover hidden patterns governing systems evolution simultaneously. This paper discusses the necessity to adapt and combine CT and ML to obtain a more realistic representation of MC market behavior to forecast long-term price trends.

Scenario-based Optimal Real-time Charging Strategy of Electric Vehicles with Bayesian Long Short-term Memory Networks

Joint operation optimization for electric vehicles(EVs)and on-site or adjacent photovoltaic generation(PVG)are pivotal to maintaining the security and economics of the operation of the power system concerned.Conventional offline optimization algorithms lack real-time applicability due to uncertainties involved in the charging service of an EV charging station(EVCS).Firstly,an optimization model for real-time EV charging strategy is proposed to address these challenges,which accounts for environmental uncertainties of an EVCS,encompassing EV arrivals,charging demands,PVG outputs,and the electricity price.Then,a scenario-based two-stage optimization approach is formulated.The scenarios of the underlying uncertain environmental factors are generated by the Bayesian long short-term memory(B-LSTM)network.Finally,numerical results substantiate the efficacy of the proposed optimization approach,and demonstrate superior profitability compared with prevalent approaches.

Optimal Pricing Strategy for Data Center Considering Demand Response and Renewable Energy Source Accommodation

With the continuous development of information technology,data centers(DCs)consume significant and evergrowing amounts of electrical energy.Renewable energy sources(RESs)can act as clean solutions to meet this requirement without polluting the environment.Each DC serves numerous users for their data service demands,which are regarded as flexible loads.In this paper,the willingness to pay and time sensitivities of DC users are firstly explored,and the user-side demand response is then devised to improve the overall benefits of DC operation.Then,a Stackelberg game between a DC and its users is proposed.The upper-level model aims to maximize the profit of the DC,in which the time-varying pricing of data services is optimized,and the lower-level model addresses user’s optimal decisions for using data services while balancing their time and cost requirements.The original bi-level optimization problem is then transformed into a single-level problem using the Karush-Kuhn-Tucker optimality conditions and strong duality theory,which enables the problem to be solved efficiently.Finally,case studies are conducted to demonstrate the feasibility and effectiveness of the proposed method,as well as the effects of the time-varying data service price mechanism on the RES accommodation.

Methodological choices of HR research conducted in Asia

A twenty-year study of the Human Resource （HR） practices- outcome relationship has found that more rigorous methodologies have been adopted over time. However, several problematic features such as cross-sectionaL single-informant, and single-level designs continue to be adopted （Bainbridge et al., Human Resource Management, 2016）. Responding to calls for increased contextualization of research by investigating the relationship between the location of data collection and the methodological choices of researchers, this study answers the question ＂How unique are the methodological choices of HR research conducted in Asia？＂ Applying content analysis to 241 published articles, we compare internal, external, construct and statistical conclusion validity of studies collected in North America （n^66）, Europe （n=95） and Asia （n=80, including 57 studies from China）. Results show that despite similarities in cross-sectional, single-informant and single-level designs across regions, research conducted in Asia is mainly undertaken via field studies, using subjective outcome measures at the organizational level, following a post-predictive design. In addition, studies from Asia are more recent, and show a shorter time gap between data collection and publication. Theoretical and practical implications embedded in the dynamic context of Asia in general, and China more specifically are discussed.