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This work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series.

AbstractThis work reports the synthesis, characterization, photophysical, and photovoltaic properties of five new thieno[3,2‐b][1]benzothiophene isoindigo (TBTI)‐containing low bandgap donor–acceptor conjugated polymers with a series of comonomers and different side chains. When TBTI is combined with different electron‐rich moieties, even small structural variations can have significant impact on thin film morphology of the polymer:phenyl C70 butyric acid methyl ester (PCBM) blends. More importantly, high‐resolution electron energy loss spectroscopy is used to investigate the phase‐separated bulk heterojunction domains, which can be accurately and precisely resolved, enabling an enhanced correlation between polymer chemical structure, photovoltaic device performance, and morphology.

Abstract Standardization of biogas technology is immensely important for the promotion of the biogas industry worldwide. China has built a complete biogas standard system, which is divided into common, household biogas, biogas engineering, biogas digester for domestic sewage treatment, output utilization, and service system standards. The problems and potential barriers for biogas standardization in China are analyzed and come down to sluggish standard, overlapped standard, government-dominated standard, and lagging international standard. Accordingly, all potential biogas standards should be evaluated and placed under the same department. China Biogas Society and China Association of Rural Energy Industry play leading roles in developing enterprise or group/association biogas standards and ISO biogas standards. The bio-natural gas standard system and experimental standardization should be developed as well to replenish biogas standard system. A paradigm shift in biogas standardization should be from government-dominated to market-oriented model. The lessons learned for other developing countries includes expanding standardization to multi-aspects to realize full lifecycle control and management, building rapid responding mechanism of standardization to adopt industry transformation, integrating outdated standards into new versions, and establishing market-based standard system.

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact. Funding Agencies|VinnovaVinnova [2017-01649]

Abstract There is strong agreement across the energy modelling community that wind energy will be a key route to mitigating carbon emissions in the electricity sector. This paper presents a Geospatial Information System methodology for estimating spatially-resolved levelised cost of electricity for offshore wind, globally. The principal spatial characteristics of capital costs are transmission distance (i.e. the distance to grid connection) and water depth, because of the disparate costs of turbine foundation technologies. High resolution capacity factors are estimated from a bottom-up estimation of global wind speeds calculated from several decades of wind speed data. A technology-rich description of fixed and floating foundation types allows the levelised cost of electricity to be calculated for 1 × 1 km grid cells, relative to location-specific annual energy production, and accounting for exclusion areas, array losses and turbine availability. These data can be used to assess the economically viable offshore wind energy potential, globally and on a country basis, and can serve as inputs to energy systems models.

Conjugated polymers with glycol-based chains, are emerging as a material class with promising applications as organic mixed ionic-electronic conductors, particularly in bioelectronics and thermoelectrics. However, little is still known about their microstructure and the role of the side chains in determining intermolecular interactions and polymer packing. Here, we use the combination of electrospray deposition and scanning tunneling microscopy to determine the microstructure of prototypical glycolated conjugated polymers (pgBTTT and p(g2T-TT)) with submonomer resolution. Molecular dynamics simulations of the same surface-adsorbed polymers exhibit an excellent agreement with the experimental images, allowing us to extend the characterization of the polymers to the atomic scale. Our results prove that, similarly to their alkylated counterparts, glycolated polymers assemble through interdigitation of their side chains, although significant differences are found in their conformation and interaction patterns. A model is proposed that identifies the driving force for the polymer assembly in the tendency of the side chains to adopt the conformation of their free analogues, i.e., polyethylene and polyethylene glycol, for alkyl or ethylene glycol side chains, respectively. For both classes of polymers, it is also demonstrated that the backbone conformation is determined to a higher degree by the interaction between the side chains rather than by the backbone torsional potential energy. The generalization of these findings from two-dimensional (2D) monolayers to three-dimensional thin films is discussed, together with the opportunity to use this type of 2D study to gain so far inaccessible, subnm-scale information on the microstructure of conjugated polymers.