Electrification of transportation is gaining traction as a viable alternative to vehicles that use fossil-fuelled internal combustion engines, which are responsible for a major part of carbon dioxide emissions. This global turn towards electrification of transportation is leading to an exponential energy and power demand, especially during late-afternoon and early-evening hours, that can lead to great challenges that electricity grids need to face. Therefore, accurate estimation of Electric Vehicle (EV) charging loads and time of use is of utmost importance for different participants in the electricity markets. In this paper, a scalable methodology for detecting, from smart meter data, household EV charging events and their load consumption with robust evaluation, is proposed. This is achieved via a classifier based on Random Decision Forests (RF) with load reconstruction via novel post-processing and a regression approach based on sequence-to-subsequence Deep Neural Network (DNN) with conditional Generative Adversarial Network (GAN). Emphasis is placed on the generalisability of the approaches over similar houses and cross-domain transferability to different geographical regions and different EV charging profiles, as this is a requirement of any real-case scenario. Lastly, the effectiveness of different performance and generalisation loss metrics is discussed. Both the RF classifier with load reconstruction and the DNN, based on the sequence-to-subsequence model, can accurately estimate the energy consumption of EV charging events in unseen houses at scale solely from household aggregate smart meter measurements at 1–15 min resolutions.

In body-centric communications, energy efficiency is the most critical parameter, while the maximum achievable data rate is not of primary concern. In this paper we present a novel modulation scheme, which can be efficiently used in body-centric terahertz (THz) nanonetworks. This scheme is a combination of the time-spread On-Off keying (TS-OOK) and the pulse position modulation (PPM). The new modulation scheme presents lower energy consumption levels at a small cost to the achievable data rate. Furthermore, another important aspect is that, due to the nature of noise in THz communications, the proposed modulation is affected in a much smaller way by the noise. Finally, we present analytical and simulation results in order to compare the new scheme with TS-OOK.

In body-centric communications, energy efficiency is a critical performance metric, while the achievable data rate is not of primary concern. In this paper we present a novel modulation scheme, which can be efficiently used in body-centric terahertz (THz) nanonetworks. The proposed scheme is a combination of the time-spread On-Off keying (TS–OOK) and the pulse position modulation (PPM) and presents lower energy consumption, compared to other existing methods as TS–OOK, at a minor cost to the data rate. Furthermore, another important aspect is that the proposed modulation scheme can be effectively used to mitigate the impact of the specific kind of noise in THz body-centric communications, thus leading to better error performance. Finally, we present analytical and simulation results in order to compare the new scheme with the existing TS–OOK.

Emerging concepts for sustainable urban development, such as Positive Energy Districts/Neighbourhoods (PEDs/PENs), are being implemented in Europe in light of the goal of net-zero carbon emissions by 2050. From the building to the district/neighbourhood scales, these projects are fostering the implementation of local Renewable Energy Sources (RES), encouraging the adoption of Electric Vehicles (EVs) and Smart Home Technologies (SHTs). In this context, a systemic analysis of energy prosumption in these new urban areas is of paramount importance to evaluate their social and technical impacts. This dataset from a Norwegian PEN enables such systemic analysis by bringing the following quantitative and qualitative data: (i) the aggregate loads of six households, (ii) the estimated energy production based on the installed solar panel capacity, (iii) the labelling of individual activities, (iv) solar irradiance and weather data, (v) time of use of electric appliance surveys, and (vi) in-depth semi-structured interviews on household energy practices. Therefore, this dataset facilitates, multi-disciplinary research on the sociotechnical aspects of PENs and net-positive buildings for their design and implementation, helping to answer the “what”, “why”, and “how” of energy prosumption.