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Smart meters are devices that provide detailed information about the energy consumed by specific electricity supply points, such as homes, offices, and businesses. Data from smart meters are useful for modeling energy systems, predicting electricity consumption, and understanding human behavior. We present the first smart meter dataset from Spanish electricity supply points, expanding the geographic diversity of available data on energy consumption at the household level and reducing biases in existing data, which typically come from a limited number of countries. The dataset consists of 25,559 raw hourly time series with an average length of nearly three years, spanning from November 2014 to June 2022. It also includes three subsets obtained by segmenting and cleaning the raw time series data, each focusing on the periods before, during, and after the COVID-19 lockdowns in Spain. This dataset is a valuable resource for studying electricity consumption patterns and behaviors that emerge in response to different natural experiments, such as nationwide and regional lockdowns, nighttime curfews, and changes in electricity pricing. [ABSTRACT FROM AUTHOR]

A rise in the number of EVs (electric vehicles) in Europe is putting pressure on power grids. At an urban scale, Positive Energy Districts (PEDs) are devised as archetypes of (small) urban districts managing a set of interconnected buildings and district elements (lighting system, vehicles, smart grid, etc.). This paper offers a comprehensive analysis of the impact of e-mobility in a PED, simulated using MATLAB-Simulink software. The PED, a small district in northern Spain, is assessed in five scenarios representing varying requirements in terms of energy efficiency of buildings, type of street lighting and number of EVs. The results suggest that the energy rating of the buildings (ranging from A for the most efficient to E) conditions the annual energy balance. A PED with six interconnected buildings (3 residential and 3 of public use) and 405 EVs (as a baseline) only achieves positivity when the buildings have a high energy rating (certificate A or B). In the most efficient case (A-rated buildings), simulation results show that the PED can support 695 EVs; in other words, it can provide nearly 9 million green kilometres. This result represents a potential 71% saving in carbon emissions from e-mobility alone (as compared to the use of fossil-fuel vehicles), thus contributing a reduction in the carbon footprint of the district and the city as a whole. [ABSTRACT FROM AUTHOR]

The Food and Agriculture Organization of the United Nations estimated that about 1.3 billion tons of food produced for human consumption was lost or wasted globally. Thus, the reduction of the current food loss and waste along the agrifood chain is becoming a priority, both for optimization of resources and reduction waste generation costs. For this purpose, the first step is to quantify the food wastage generation to be able to identify corrective measures. However, in spite of the considerable efforts already undertaken to establish common methodologies to measure the food wastage at different geographical scales, there are still some gaps and inconsistencies. In this regard, the information gathering is labour-intensive because of the different actors involved. The creation of new methodologies and tools capable of automatically identifying these agents would be of great value so as to subsequently apply the more appropriates quantification methodologies. This work aims at providing a new methodology to facilitate this process thanks to the previous identification and classification of the potential food wastage generators. As a result, it provides baseline information for one of the earliest steps of the food wastage quantification process, which is the establishment of the scope of the food wastage inventory. The baseline data needed is taken from the Statistical classification of economic activities in the European Community (NACE), particularly from the most disaggregated level called "classes". This information has been combined with data from the trading income tax at municipal scale thanks to the use of Geographic Information Systems (GIS) and the common codes for NACE classes, generating a visual tool for the localization of points with potential of food-wastage generation and their weight of each economic activity over the agrifood chain. The proposed methodology has been implemented for the real case of the municipality of Zamudio (Spain) and it has allowed the identification of the different entities linked with economic activities that are potential generators of food wastage, the weight of each activity over the entire agrifood chain, and the geographical location of these entities in the territory. Furthermore, this methodology was used to compare the nature and number of these activities in another municipality (Karrantza, Spain) and it has also been applied to the entire region of the Basque Country (Spain)., (Copyright © 2018 Elsevier Ltd. All rights reserved.)