Data for global power demand and solar PV output matching

Increasing use of solar energy necessitates better data sets for analyzing matching of solar photovoltaic output and power demand. Data source information presented in this article is useful to analyze the self-consumption rates of photovoltaic systems on global scale. The data is provided in figure format. The time resolution is basically one hour, but 1-min data is also included. The geographical range of selected sites is lat. 26–60 deg. (Europe, Asia, Latin-America). The power demand data ranges from a single household to national scale. Both measured and simulated data are included. The data sets are linked to a recent article by Lund [1].


a b s t r a c t
Increasing use of solar energy necessitates better data sets for analyzing matching of solar photovoltaic output and power demand. Data source information presented in this article is useful to analyze the self-consumption rates of photovoltaic systems on global scale. The data is provided in figure format. The time resolution is basically one hour, but 1-min data is also included. The geographical range of selected sites is lat. 26-60 deg. (Europe, Asia, Latin-America). The power demand data ranges from a single household to national scale. Both measured and simulated data are included. The data sets are linked to a recent article by Lund [1].
& 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Value of the data
The data is helpful for performance simulations of photovoltaic (PV) systems The data is applicable for a range of different PV systems The data can be used for planning more effective PV systems

Data
The dataset of this article provides information on the solar photovoltaic (PV) system output and power demand for selected sites and load types over an entire year used in [1].

Experimental design, materials, and methods
The details of the data sets are described in Table 1. The location of the site is indicated for each data set and the sources for raw data. Different time scales (short-long), geographical range (northsouth), spatiality (building-city-country), and climate zones (cold-hot) are covered. The range of the data is typically one year with 1-hour resolution, but for a few data sets the time resolution is 1 min or 10 min.
The PV output data is for a 30-degree tilted surface orientated to the south. The 25-year solar data set for Helsinki (Finland) is for years 1973-1997 and this data is for a horizontal surface only. Except for one data set (Helsinki) with 1-min resolution, all other data is hourly.          The power demand (load) data is for hourly demand over 1 year, except for one dataset with 1-min resolution for a building in Helsinki, and for two data sets in Sweden (L1,L2) with 10-min resolution.
The household, building, regional, and national load profiles (Eastern Saudi-Arabia, Italy, Austria, Finland, Helsinki/building, Sweden/buildings L1, L2) are based on measured data, whereas the load profiles of the cities are based on simulated spatiotemporal load profiles (Conception/Chile, Delhi/India, Helsinki/ Finland, Shanghai/China). The method employed to generate these hourly profiles is explained in [7,12]. The city, regional, and national load profiles are aggregated demands of the whole electricity sector.
Sweden load L1 (see Table 1) represents a single household load based on appliances and lighting, whereas Sweden L2 is a block of houses with a stronger seasonal component (electric heating).
The PV output is calculated with a simulation tool (ALLSOL) [13] from measured solar radiation (reference to solar data given in Table 1) and ambient temperature data. For PV technology, a standard Si-module is used. The PV-output is modeled as temperature dependent.

Transparency document. Supporting information
Transparency data associated with this article can be found in the online version at doi:10.1016/j. dib.2018.06.054.