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In the pursuit of sustainable development, accurate renewable energy demand forecasting holds great significance for climate change mitigation and promoting sustainability. However, renewable energy forecasting has been consistently challenged by seasonality and nonlinearity. Identifying the periodic and nonlinear characteristics concealed within renewable energy sources accurately is still an unexplored problem. Consequently, an innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting, which incorporates seasonal dummy variables and a power exponent term for handling the seasonality and nonlinear patterns in time series. Furthermore, an intelligent algorithm matching framework is proposed to augment the flexibility of the newly developed model. For practical purposes, the new methodology is contrasted against a range of benchmarks encompassing statistical, machine-learning, and traditional grey models in forecasting the quarterly total renewable energy consumption in the United States. The proposed model exhibits over 27% improvement rates over its counterparts, achieving the most superior predictive accuracies of 1.45%, 39.27, and 0.79 in MAPEP , RMSEP , and MASEP metrics, respectively. Furthermore, the probability density and sample size analyses are conducted to validate the robustness of the new model, confirming its adaptability and stability towards algorithm randomness and historical information volume. Consequently, the novel model is employed to forecast the short-to-long-terms renewable energy consumption in the U.S., showcasing an upward trend and seasonal fluctuations of the consumption for the forthcoming 24 quarters from 2023Q4 to 2029Q3. These insights can offer valuable implications to the stakeholders such as energy suppliers, utility managers, and policy advocates, highlighting actionable strategies for optimizing renewable energy consumption forecasting and aiding sustainable development initiatives. • An innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting. • The optimum algorithm matching the novel model is determined from five alternatives. • The Monte Carlo Simulation and parameter sensitivity analysis advance the robustness. • PSO with the optimal parameter group has been selected for searching coefficients. • The new model possesses higher accuracy and robustness than various competitors. [ABSTRACT FROM AUTHOR]

Woody biomass energy makes up a large share of renewable energy consumption in the EU, and the largest share in countries like Denmark. However, little is known about how the public perceive it. This paper presents the results of a questionnaire-based survey of the public in Denmark focusing on attitudes toward woody biomass for energy. The main findings are that, while more people favor than oppose it, a large proportion are undecided about the use of woody biomass. The survey also revealed a lack of knowledge about this energy source. A Best-Worst Scaling methodology revealed that Danes prioritize environmental sustainability concerns – namely biodiversity loss, the hindering of the development of other renewable energy sources and the climate change mitigation potential of woody biomass – over societal and/or economic ones when it comes to the promotion of woody biomass. Finally, it was found that concern about climate change, belief in the mitigation potential of woody biomass, and being younger explained the formation of positive attitudes toward woody biomass. Policy makers aiming to take public concerns into account in discussions about Denmark's energy future should focus more on minimizing the negative environmental impacts associated with woody biomass rather than on its economic benefits. [Display omitted] [ABSTRACT FROM AUTHOR]

Anthropogenic activities and advancements in industries boost global energy demand and increase fossil fuel consumption, causing several global environmental problems, such as climate change. As a climate change mitigation strategy, the use of renewable energy technologies has gained unprecedented interest. In particular, the thermochemical processing of lignocellulosic biomass integrated with other renewable energy technologies has emerged rapidly. It is critical to select appropriate integrated renewable energy system configurations for sustainable and feasible power generation towards higher environmental benefits. Understanding the possible configurations of thermochemical lignocellulosic biomass processing technologies (gasification, pyrolysis, hydrothermal gasification, or hydrothermal carbonization) integrated with renewable energy technologies (solar thermal, fuel cell, fusion power, or energy storage) is crucial for the further development and propagation of the integrated renewable energy system. Hence, we provide a systematic review of the thermochemical conversion of lignocellulosic biomass integrated with the other renewable energy technologies. Finally, the challenges associated with the implementation of these systems and suggestions for future research on the systems are discussed. • Renewable energy technology is integrated with thermochemical biomass conversion process. • The integrated bioenergy systems help overcome limitations of single-source energy systems. • Challenges and prospects for the integrated systems are highlighted. [ABSTRACT FROM AUTHOR]

• Feasibility of net zero-energy (nZE) for residential and commercial building stock. • Accommodation of nZE conditions by current power distribution network. • Proposed framework integrated synthetic population and activities. • Case study on dense urban area in noncold climate. • nZE transformation pathways explored as different from those for cold climate. The transformation to net zero-energy (nZE) building stocks involves harvesting of renewable energy, enhancement of building energy efficiency, incorporation of various supply-side options, and advanced energy management. Although several studies have evaluated the feasibility of nZE for residential areas in cold regions, only a few studies have been conducted on dense urban regions in noncold climate. Additionally, the accommodation of such building stock transformation by the current power distribution network has not been clarified to date. Thus, this study proposes a novel framework integrating synthetic population, activity, building stock, and power distribution network to explore transformation pathways. This was further demonstrated by a case study on a densely populated region covering four cities in Tokyo, Japan. The results signified that complete dissemination of popular energy efficiency measures can reduce the energy demand by 40%. With the reduction in energy demand, nearly nZE condition can be realized only if the building roof area is completely covered by photovoltaics. In particular, one half of the generation would be utilized locally, whereas the remaining would be exported. Moreover, the power distribution network can accommodate the transformation with a slight increase in power distribution loss up to 1% and a modest reinforcement requirement owing to line overloading. Conclusively, this study revealed that the transformation and reinforcement requirement are significantly distinct from those required in cold regions. [ABSTRACT FROM AUTHOR]

Climate change mitigation has been a global effort and energy generation is one of the primary sources of GHG emissions. There is a need to transition from fossil-based energy to renewable-based energy. One of the platforms to nourish the work is Eco-Industrial Park (EIP) which it promotes resource efficiency, industrial symbiosis, and circular economy practice. There is a need to explore an optimal configuration of multiple renewable energy (RE) systems in a centralised EIP facility that generates electricity for internal consumption and exports the excess energy to the grid. This model developed a mixed-integer non-linear programming (MINLP) model that considers RE from biogas, biomass, micro-hydro, and solar sources for a centralised water hub. The energy generated is primarily used for the main processes involved in the facility as the excess energy will be supplied to the grid. A case study and two scenarios were assessed, and the results offered simultaneous technical, economic, and environmental benefits, which are in the forms of multiple RE generations, additional yearly profit, and GHG emission avoidance from fossil source. This will further strengthen the envisioned role of EIP as it is also capable of supporting the United Nations Sustainable Development Goal (UNSDG) and RE100 agenda. • A MINLP model to optimise renewable energy generation for Total Site was developed. • The model is a part of a framework to optimise energy, water, and material streams. • The model considers energy generation via solar, micro hydro, biomass, and biogas. • The proposed installation could suffice the core processes energy needs. • Additional profit is up to 39% and GHG emission reduction can be up to 70%. [ABSTRACT FROM AUTHOR]

Renewable energy plays a crucial role in both climate change mitigation and adaptation in highly climate-vulnerable nations such as Nepal. This paper reviews various types of renewable energy technologies and their status, potential for adoption, relationship to climate change, and mitigative and adaptive roles in Nepal. Nepal has installed micro-hydro projects, solar power, improved cooking stoves, biogas technology, improved water mills, and wind energy to mitigate and adapt to climate change. There is a growing potential for renewable energy development in Nepal, such as hydropower, solar, wind energy, biogas, and improved cooking stoves. Roughly 70% of Nepal's energy consumption is generated from traditional energy sources while renewable energy accounts for approximately three percent. The gradual increase in the use of renewable energy has reduced greenhouse gas emissions and enhanced carbon sequestration. By adopting renewable energy technologies, Nepal has reduced emissions by 221,129 tCO 2 e from 2017 to 2018. Nepal's second Nationally Determined Contribution targets a 15% increase in national energy use from renewables with a reduction of 23% of CO 2 emissions by 2030 using biogas and improved cooking systems. Furthermore, a significant increase in the adoption of renewable energy has become a pivotal strategy in adaptation to climate change in social, health, and economic sectors resulting in time savings, alternative income sources, improved health and educational status, local job opportunities, and the promotion of social capital. The benefits of adapting to climate change and mitigating CO 2 emissions via renewable energy are significant at the local, national, and international levels. This study recommends that the government of Nepal focus work on energy policy reviews to address local energy demand and climate change issues by utilizing renewable energy resources at the local level, which has global implications. • Suitable, potential renewable energy technologies in Nepal. • The relationship between energy and climate change. • Relevant data on energy and climate change in tables and figures. • Social, economic, and environmental benefits of renewable energy. • Climate change adaptation and mitigation via renewable energy. [ABSTRACT FROM AUTHOR]