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Energy demand modelling has been widely applied in various contexts, including power plant generation, building energy simulation and demand-side management. However, it is still an ongoing research topic in terms of the choice of modelling method, feature engineering for data-driven methods, the application contexts and the type of data used. In the residential sector, survey-based and meter-based approaches are categorised according to the type of input data used, i.e. the activity records from the time use survey and energy consumption from meters respectively. These two paradigms are not necessarily easy to combine, which warrants the questions of when one may be preferred over the other and whether they need to be combined despite the significant data requirements. Other details also have a huge impact on the data structure and performance of the energy demand model, including the choice of influential factors, the historical time window of factors selected, the split between training and test data, and the choice of machine learning (ML) algorithm. There is a lack of comparative research to guide researchers and practitioners in developing energy demand modelling capability, specifically as it pertains to these issues. This study analyses three groups of test scenarios in a multi-household residential context based in the UK. Six ML algorithms (LightGBM, Random forest, ANN, SVM, KNN and LSTM), with eight sets of various influential features, at four different historical time window widths and two train-test splits were compared. An appropriate methodology was designed to capture the temporal impact of activities on energy demand and represent the overlap and interaction of activities. The results show that the combination of meter-based and survey-based energy demand models performs better in terms of modelling accuracy and robustness against sudden load variation. Particularly, integrating energy tariffs, household and individual attributes, appliance usage and general activity features can improve the energy demand model. Among the ML algorithms, LightGBM and ANN perform better than other algorithms while LSTM and SVM may not be suitable in this multi-household short monitoring context.

The united nations educational, scientific and cultural organization (UNESCO) considers the historic urban landscapes as the world heritages. Managing historic city centers and maintaining historic cores are the emerging challenges for sustainable urban planning. Today, the historic cores form an important part of the economic, social, environmental, and physical assets and capacities of contemporary cities, and play a strategic role in their development. One of the most important approaches to the development of central textures, especially in historical and cultural cities, is the sustainable urban regeneration approach, which encompasses all aspects of sustainability, such as the economic, social, cultural and environmental aspects. To maintain sustainability and regeneration of historic cores of cities, it is necessary to provide insight into the underlying characteristics of the local urbanization. Furthermore, the fundamental assets are to be investigated as indicators of sustainable regeneration and drivers of urban development. In the meantime, a variety of research and experience has taken place around the world, all of which has provided different criteria and indicators for the development of strategies for the historic cores of cities. The present study, through a meta-analytic and survey method, analyzing the experience and research reported in 139 theoretical and empirical papers in the last twenty years, seeks to provide a comprehensive conceptual model taking into account the criteria and indices of sustainable regeneration in historic cores of cities. The quality of the survey has been ensured using the preferred reporting items for systematic reviews and meta-analysis (PRISMA).

Global demand for both clean energy carriers and agricultural nutrients continues to grow rapidly, alongside increasing quantities of waste globally, interlinked challenges that may be addressed with interlinked solutions. We report on the potential efficiency and Greenhouse Gas (GHG) intensity of several configurations of a new, sustainability-driven ammonia (NH3) production processes to determine whether a waste-based process designed first around carbon dioxide (CO2) capture can compete with other available NH3 technologies. This is assessed via different scenarios: Two hydrogen generating options are paired with four CO2 fates. For either an anaerobic digestion-centered process or a two-stage dark fermentation coupled with anaerobic digestion process, the resultant CO2 may be captured and injected, sold to the marketplace, released directly in the atmosphere, or converted to urea in order to produce a green substitute for synthetic NH3. Modeled yields range from 47 t NH3 when the resultant CO2 is released or captured, or 3.8 t NH3 and 76.5 t urea when the system is designed to produce no unutilized CO2. Among the technologies assessed, NH3 production where CO2 is captured for anaerobic digestion-only is the most efficient for GHG emissions and water consumption, while the two-stage requires less energy on a fertilizer-N basis. GHG emissions for anaerobic digestion-only are approximately 8% lower than the two-stage. The best of the proposed technology configurations consumes about 41% less energy than water electrolysis coupled with Haber-Bosch and approximately 27% lower energy than Steam Methane Reforming (SMR) coupled with Haber-Bosch per kg NH3.

The importance of LCEM (life cycle energy management) has been recognized from the view of life cycle energy savings for sustainable buildings. The purposes of this research are the proposal of an LCEM framework and development of prototype HVAC system simulation tools for LCEM. In this paper, the necessity of energy simulation tools for LCEM is discussed, and the outline and solution method of the simulation tool are shown.

A brisk building boom of hydropower mega-dams is underway from China to Brazil. Whether benefits of new dams will outweigh costs remains unresolved despite contentious debates. We investigate this question with the "outside view" or "reference class forecasting" based on literature on decision-making under uncertainty in psychology. We find overwhelming evidence that budgets are systematically biased below actual costs of large hydropower dams - excluding inflation, substantial debt servicing, environmental, and social costs. Using the largest and most reliable reference data of its kind and multilevel statistical techniques applied to large dams for the first time, we were successful in fitting parsimonious models to predict cost and schedule overruns. The outside view suggests that in most countries large hydropower dams will be too costly in absolute terms and take too long to build to deliver a positive risk-adjusted return unless suitable risk management measures outlined in this paper can be affordably provided. Policymakers, particularly in developing countries, are advised to prefer agile energy alternatives that can be built over shorter time horizons to energy megaprojects.

This Article examines the genesis and context of SE4All, placing the effort within both itshistorical and international policy contexts. It highlights the voluntary nature of the initiative andargues that its effective implementation and the achievement of its goals require the articulation ofan applicable international legal framework that aids the transformation of SE4All’s policy actionsinto binding international legal commitments. The article contends that such a transformation doesnot depend on the creation of entirely new legal rules or institutions. Instead, an effective frame-work for successful implementation of SE4All can be derived from existing rules of internationalhuman rights law and sustainable development law. Reliance on these twin bodies of interna-tional law will increase the prospects for SE4All to achieve energy access and related goals thatits predecessor initiatives have failed to accomplish

Sewage sludge handling is becoming a concern in Europe due to its increasing amount and the presence of contaminants, such as heavy metals and pharmaceutical and personal care products (PPCPs). Currently, over 70% of sludge in Europe is treated thermally by incineration or used as fertilizer in agriculture. New thermochemical methods are under development and are expected to be implemented in the near future. This paper considers the life cycle environmental impacts of the following five alternatives for sludge handling, taking into account the presence of heavy metals and PPCPs: i) agricultural application of anaerobically digested sludge; ii) agricultural application of composted sludge; iii) incineration; iv) pyrolysis; and v) wet air oxidation. The results suggest that anaerobic digestion with recovery of nutrients and electricity has the lowest environmental impacts in 11 out of 18 categories considered. For the mean to maximum resource recovery, composting is the worst alternative, followed by pyrolysis with lower recovery rates. Agricultural application of anaerobically digested sludge has the highest freshwater ecotoxicity due to heavy metals, unless their concentration is in the lowest range, as found in some European sewage sludge applied on land. Therefore, stricter control of heavy metals in the sludge is needed for this option to limit freshwater ecotoxicity to the levels comparable with the thermal processes. The results also indicate that PPCPs have a negligible contribution to freshwater ecotoxicity when compared to heavy metals in the anaerobically digested sludge. Since thermal processes are currently drawing attention due to their potential benefits, the findings of this work suggest that their adoption is environmentally beneficial only if high resource recovery rates can be achieved.