Abstract:In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC) technique and the traditional multiple regression (PC-regression), for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004-2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.
The Efficient Market Hypothesis (EMH) is widely accepted to hold true under
certain assumptions. One of its implications is that the prediction of stock
prices at least in the short run cannot outperform the random walk model. Yet,
recently many studies stressing the psychological and social dimension of
financial behavior have challenged the validity of the EMH. Towards this aim,
over the last few years, internet-based communication platforms and search
engines have been used to extract early indicators of social and economic
trends. Here, we used Twitter's social networking platform to model and
forecast the EUR/USD exchange rate in a high-frequency intradaily trading
scale. Using time series and trading simulations analysis, we provide some
evidence that the information provided in social microblogging platforms such
as Twitter can in certain cases enhance the forecasting efficiency regarding
the very short (intradaily) forex.Comment: This is a prior version of the paper published at NETNOMICS. The
final publication is available at
http://www.springer.com/economics/economic+theory/journal/1106
In this paper a multiple-input multiple-output (MIMO) simulation model for vehicle-to-vehicle (V2V) communication channels in an urban cross-junction scenario is presented. The model is an extension and modification of an existing Tjunction model in the literature. Four propagation processes are considered: line-of-sight (LOS), single bounce reflections from side walls, double bounce reflections from side walls, and single bounce reflections from the corner of the building in front of the transmitter (TX) and receiver (RX). Each propagation process is linked to a cluster of scatterers, with a cluster size that varies with respect to the position of the TX and RX. The relations between angle-of-arrivals and angle-of-departures of all Multipath Components (MPCs) are derived depending on the different positions of the TX and RX. For the single bounce reflections from the corner a new method is being used where the scatterers are distributed randomly in a triangular plane, based on the assumption that corners of buildings typically have different scattering objects contributing to the reflection process. A complete expression for the time-variant transfer function is then derived by super positioning all contributions, including the LOS when this is available. The final results show that our model follows a realistic measurement based path-loss model, which subsequently makes the model a suitable candidate for analyzing MIMO V2V fading channels in cross-junction scattering environments.
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