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| 1 |
ID:
094210
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| Publication |
2010.
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| Summary/Abstract |
Electric power demand forecasts play an essential role in the electric industry, as they provide the basis for making decisions in power system planning and operation. A great variety of mathematical methods have been used for demand forecasting. The development and improvement of appropriate mathematical tools will lead to more accurate demand forecasting techniques.
In order to forecast the monthly electric power demand per hour in Spain for 2 years, this paper presents a comparison between a new forecasting approach considering vector autoregressive (VAR) forecasting models applied to interval time series (ITS) and the iMLP, the multi-layer perceptron model adapted to interval data.
In the proposed comparison, for the VAR approach two models are fitted per every hour, one composed of the centre (mid-point) and radius (half-range), and another one of the lower and upper bounds according to the interval representation assumed by the ITS in the learning set. In the case of the iMLP, only the model composed of the centre and radius is fitted. The other interval representation composed of the lower and upper bounds is obtained from the linear combination of the two.
This novel approach, obtaining two bivariate models each hour, makes possible to establish, for different periods in the day, which interval representation is more accurate. Furthermore, the comparison between two different techniques adapted to interval time series allows us to determine the efficiency of these models in forecasting electric power demand. It is important to note that the iMLP technique has been selected for the comparison, as it has shown its accuracy in forecasting daily electricity price intervals.
This work shows the ITS forecasting methods as a potential tool that will lead to a reduction in risk when making power system planning and operational decisions.
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| 2 |
ID:
150806
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| Summary/Abstract |
The Russian economy is highly responsive to oil price fluctuations. At the start of 2014, the country was already suffering from the weak economic growth, partly due to the ongoing crisis in Ukraine and Western sanctions. The recent plunge in global oil prices put even further strain on the Russian economy. This paper analyzes the dynamic relationship between oil price shocks, economic sanctions, and leading macroeconomic indicators in Russia. We apply a vector autoregression (VAR) to quantify the effects of oil price shocks as well as western economic sanctions on real GDP, real effective exchange rate, inflation, real fiscal expenditures, real consumption expenditures, and external trade using quarterly data from 1999:1 until 2015:1. Our results show a significant impact of oil prices on the Russian economy. We predict that Russia’s economic outlook is not very optimistic. If sanctions remain until the end of 2017, the quarter-to-quarter real GDP will contract on average by 19 percent over the next two years.
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