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Increasing concerns about sustainability and energy conservation, coupled with the proliferation of incentives in the EU to achieve energy savings, suggest that significant improvements in energy efficiency should be realized. A policy measure that should have a direct impact on energy savings is the replacement of incandescent and halogen light bulbs by more efficient lighting technologies, which was implemented in 2009. Due to the lack of detailed data, it is not feasible to measure the effect of energy-efficient improvements on electricity consumption at the aggregate level using a bottom-up approach. To overcome this limitation, this paper analyzes hourly electricity demand in a very specific period of the day: the transition from day to night. In this short period, it is plausible that lighting is the main driver of changes in electricity demand, thus making it possible to estimate the increase in electricity consumption when lights are switched on and to analyze the effects of higher energy efficiency in lighting, if any. The results of the analysis for Spain show that during the periods 2009–2011 and 2015–2016, an estimated energy savings of 251 GWh can be attributed to a reduction in the magnitude of the lighting effect, which accounts for 20.3% of the observed decrease in electricity consumption during these two periods. [ABSTRACT FROM AUTHOR]

This paper proposes a model for short-term electricity load with differentiated temperature and daylight effects by time of day, which are determined by variations in intraday economic activity. The relationship between electricity load and economic activity implies that the electricity demand response to changes in exogenous variables like temperature is non-linear as well as non-homogeneous along the day. The proposed framework, a smooth transition regression model with double threshold (LSTR2), models the observed intraday patterns in load curves to explicitly capture the effect of the circadian rest-activity cycle on the distinct responses of electricity demand to temperature and daylight variations throughout the day. The model shows that the sensitivity of demand to low temperatures is significantly larger in the "active" compared to the "rest" state. If temperatures decrease from 10 °C to 0 °C, electricity demand in the "active" state increases by 960.5 MW h per 1 °C decrease, but by only 26.6 MW h per 1 °C decrease in the "rest" state. When temperatures are higher, in the "rest state" demand decreases by 602.9 MW h per 1 °C if temperature falls from 26 °C to 21 °C, while in the "active" state demand only decreases by 323.6 MW h per 1 °C variation. Hourly electricity load (MWh logs) and temperature. Estimated electricity demand response for the "rest" and "active" state of economic activity. Image 1 • Load data shows that the temperature effect is both non-linear and non-homogenous. • Time properties of economic activity directly translate into the shape of load curves. • Economic activity also affects the way temperature influences the electricity load. • A model for hourly electricity load with differentiated temperature effects by time of day is proposed. • The proposed model captures well the higher sensitivity of electricity demand to temperatures in the "active" state. [ABSTRACT FROM AUTHOR]

The treatment of trend components in electricity demand is critical for long-term peak load forecasting. When forecasting high frequency variables, like daily or hourly loads, a typical problem is how to make long-term scenarios - regarding demographics, GDP growth, etc. - compatible with short-term projections. Traditional procedures that apply de-trending methods are unable to simulate forecasts under alternative long-term scenarios. On the other hand, existing models that allow for changes in long-term trends tend to be characterized by end-of-year discontinuities. In this paper a novel forecasting procedure is presented that improves upon these approaches and is able to combine long and short-term features by employing temporal disaggregation techniques. This method is applied to forecast electricity load for Spain and its performance is compared to that of a nonlinear autoregressive neural network with exogenous inputs. Our proposed procedure is flexible enough to be applied to different scenarios based on alternative assumptions regarding both long-term trends as well as short-term projections. [ABSTRACT FROM AUTHOR]

Since 1990, Spain has had one of the highest elasticities of electricity demand in the European Union. We provide an in-depth analysis into the causes of this high elasticity, and we examine how these same causes influence electricity demand in other European countries. To this end, we present an index-decomposition analysis of growth in electricity demand which allows us to identify three key factors in the relationship between gross domestic product (GDP) and electricity demand: (i) structural change; (ii) GDP growth; and (iii) intensity of electricity use. Our findings show that the main differences in electricity demand elasticities across countries and time are accounted for by the fast convergence in residential per capita electricity consumption. This convergence has almost concluded, and we expect the Spanish energy demand elasticity to converge to European standards in the near future. [ABSTRACT FROM AUTHOR]

Proper planning for the dimensions of an electricity production and transmission system requires the availability of medium- and long-term electricity demand projections that are sufficiently reliable. Generally, these projections are directly linked to the estimated growth for the whole real GDP (gross domestic product), although an in-depth historical evolution of this demand, as that given in this article, advises the explicit consideration of several determinants. The aim of this paper is to present an alternative analysis of the demand for electricity based on a simple growth rate decomposition scheme that allows the key factors behind this evolution to be identified. It is possible, taking this scheme as a starting point, to develop a long-term forecasting model to obtain projections of electricity demand given the expected evolution of the key factors. The proposed methodology is illustrated using Spain as a case study to obtain demand projections till 2030. [ABSTRACT FROM AUTHOR]

Distinct responses of electricity demand to temperature changes are usually assumed for residential, industrial, and service sectors. However, available empirical studies on the temperature effect of electricity demand provide only partial evidence or evidence at a very aggregate level. Our paper aims to partially fill this existing gap in the literature. Using disaggregated data by sectors, we carry out a comprehensive analysis of the sectorial breakdown of temperature effects on firms’ electricity demand. In-depth knowledge of sectorial demand responses to temperature changes is fundamental for improved energy planning because such responses represent a potential source of demand price inelasticity, one of the main obstacles for developing adequate demand response planning mechanisms. Our findings indicate that in Spain firms’ aggregate electricity demand is rather insensitive to temperature. However, there are marked differences among sectors, with the highest sensitivity found for firms in the service sector. We show that the aggregate insensitiveness of firms’ electricity demand to temperature is mainly due to a demand composition effect. [ABSTRACT FROM AUTHOR]

Europa tiene mercados de trabajo segmentados con empleos flexibles pero inseguros, que generan gran desigualdad entre trabajadores. El empleo a tiempo parcial puede ayudar a conciliar trabajo y vida familiar y aumentar la tasa de actividad femenina, pero también puede generar nuevas formas de desigualdad. Analizando empíricamente las transiciones laborales en Dinamarca, España, Francia, Italia y Países Bajos, este artículo muestra que los trabajadores a tiempo parcial -en su mayoría mujeres- corren un riesgo mayor de desocupación y recomienda aumentar la igualdad entre los trabajadores a tiempo parcial y a tiempo completo en relación con la estabilidad del empleo. [ABSTRACT FROM AUTHOR]