At the least sophisticated level of economic theory lies the belief that certain pairs of economic variables should not diverge from each other by too great an extent, at least in the long-run. Thus, such variables may drift apart in the short-run or according to seasonal factors, but if they continue to be too far apart in the long-run, then economic forces, such as a market mechanism or government intervention, will begin to bring them together again. Examples of such variables are interest rates on assets of different maturities, prices of a commodity in different parts ofthe country, income and expenditure by local government and the value of sales and production costs of an industry. Other possible examples would be prices and wages, imports and exports, market prices of substitute commodities, money supply and prices and spot and future prices of a commodity. In some cases an economic theory involving equilibrium concepts might suggest close relations in the long-run, possibly with the addition of yet further variables. However, in each case the correctness of the beliefs about long-term relatedness is an empirical question. The idea underlying cointegration allows specification of models that capture part of such beliefs, at least for a particular type of variable that is frequently found to occur in macroeconomics. Since a concept such as the long-run is a dynamic one, the natural area for these ideas is that of time-series theory and analysis. It is thus necessary to start by introducing some relevant time series models. Consider a single series Xf, measured at equal intervals of time. Time series theory starts by considering the generating mechanism for the series. This mechanism should be able to generate att of the statistical properties of the series, or at very least the conditional mean, variance and temporal autocorrelations, that is the 'linear properties' of the series, conditional on past data. Some series appear to be 'stationary', which essentially implies that the linear properties exist and are timeinvariant. Here we are concerned with the weaker but more technical

/pdf/developments-in-the-study-of-cointegrated-economic-variables-3ngb3it5q7.pdf

Developments in the study of cointegrated economic variables

The prediction of traffic flow volumes based on spectral analysis

Both the off-line and on-line methods for short-term electric load forecasting are reviewed. Since identifying an adequate model is the most important problem of any forecasting technique, the literature is classified according to the modeling approaches used for representing the load demand. The merits and drawbacks of each approach and how different authors have applied it to the problem under consideration are stressed. Also included is recent work where multivariable identification techniques are used to model the load demand of all the major loading nodes of a large power system. The advantages and the difficulties of applying such techniques are discussed. As a conclusion, directions for future research and future development of available algorithms, which can improve the state of the art, are suggested.

Short-Term Load Demand Modeling and Forecasting: A Review

Large quantities of weather-dependent renewable energy generation are expected in power systems under climate change mitigation policies, yet little attention has been given to the impact of long term climate variability. By combining state-ofthe-art multi-decadal meteorological records with a parsimonious representation of a power system, this study characterises the impact of year-to-year climate variability on multiple aspects of the power system of Great Britain (including coal, gas and
nuclear generation), demonstrating why multi-decadal approaches are necessary. All aspects of the example system are impacted by inter-annual climate variability, with the impacts being most pronounced for baseload generation. The impacts of inter-annual climate variability increase in a 2025 wind-power scenario, with a 4-fold increase in
the inter-annual range of operating hours for baseload such as nuclear. The impacts on peak load and peaking-plant are comparably small. Less than 10 years of power supply and demand data are shown to be insuffcient for providing robust power system planning guidance. This suggests renewable integration studies - widely used in policy, investment and system design - should adopt a more robust approach to climate characterisation.

https://iopscience.iop.org/article/10.1088/1748-9326/11/12/124025/pdf

Quantifying the increasing sensitivity of power systems to climate variability

Electricity demand forecasting over Italy: Potential benefits using numerical weather prediction models

A method is described for statistically analysing the relationship between weather and electricity demand for applications to practical load estimating by electricity supply engineers The characteristics of the specific meteorological elements to which the demand has been found to respond are discussed, particularly those affecting heating and lighting demands The thermal-lag factor appropriate to the delay in consumer response to temperature changes and the influence of the cooling power of the wind on heating load are examined In order to assess the lighting load, observations of the atmospheric transmission of light are used to derive a method for reducing cloud and visibility forecasts to their daylight illumination equivalents

M. Davies

Papers

The relationship between weather and electricity demand