Climate change and electricity consumption—Witnessing increasing or decreasing use and costs?

Energy demand for the heating and cooling of residential houses in Finland in a changing climate

“…In a previous study, the heating energy need in Finland was expected to 6 decrease by 10-14 % from 1961-1990 to 2021-2050 [9]. In another study it was assessed that, 7 at the country level, the net economic value of the decreased aggregate demand for heating 8 electricity will contribute by 2050 to approximately a 0.19% savings compared to the gross 9 domestic product in 2007 [16]. In both studies, HCED and its future changes were estimated 10 with the aid of heating and cooling degree days.…”

“…11 12 In this paper, we assess the influence of the expected climate change on the demand, delivered 13 consumption and costs of energy for the heating and cooling of a typical detached house in 14 southern Finland. The work is based on a dynamic building energy simulation program and 15 on climate change projections derived from a multi-member ensemble of global climate 16 models. We address the following research questions: a) In a region where temperature and 17 summertime solar radiation are the most influential climatic variables for HCED, how can 18 future hourly weather data required in the energy simulations be constructed?…”

“…[ standard procedure for selecting these months is to compare the frequency distributions of 14 daily climatic variables during a single month of a candidate year with the corresponding 15 distributions for the same calendar month but based on data of a long (30-year) period. The 16 months from these years having the smallest differences from the climatological distributions, 17 as quantified by the so-called Finkelstein-Schafer parameters, are selected (for details, see [1] 18 and references therein). 19 20 In this study, two sets of weather observations were applied for simulations of building 21 energy demand in southern Finland in the current (or more precisely, in the recent past) 22 climate: 1) a recently developed test reference year TRY2012 data set [1]; and 2) weather data The months used to construct TRY2012 were selected by using monthly dependent weighting 2 factors for the Finkelstein-Schafer parameters of temperature (T), relative humidity (RH), 3 global solar radiation (G) and wind speed (W) in 1980-2009 [1].…”

Comparison of Machine Learning Methods for Electricity Demand Forecasting in Bosnia and Herzegovina

“…The different approaches to determining the influence of air temperature on electricity consumption are presented in Refs. [1][2][3][4][5] and usually, all analyzed cases confirm the high interrelationships between these variables. Also, during the process of forecasting, the ambient temperature, human social activity and other variables provides important information that forecast results can make a lot more accurate.…”

Global Energy Demand in a Warming Climate

“…Previous regional and global studies focusing on residential energy use miss this heterogeneity and tend to find a prevailing negative impact on energy demand in temperate (De Cian, Lanzi and Roson, 2013) or Nordic countries (Pilli-Sihvola et al, 2010;Mima and Criqui, 2015). The sectoral dimension of our work points at commercial and industrial activities in temperate and tropical regions, respectively, as two sectors where changes in socioeconomic and climatic conditions could signficantly expand the use of electricity, leading to a net increase in energy demand in temperate regions, such as the United States, and globally.…”