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The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

• The method to effectively capture the key information of faults and improved model efficiency. • The method based on meta-learning represents an innovative path for the inverter output decision-making. • The method can quickly and accurately fit the mapping between features and decisions. With the increase of energy demand and the continuous development of renewable energy technology, active distribution networks have become increasingly important. However, the introduction of a large amount of renewable energy has made the structure of ADN increasingly complex and fragile, and emergency fault caused by emergencies may often occur. Voltage control in the emergency fault event is particularly important. In this context, this paper presents a meta-learning based voltage control strategy for renewable energy integrated active distribution network. A general regression neural network is first applied to extract features from the operation data. Then, the local cross-channel interaction network is adopted to capture targeted information that is most related to emergency fault from the features and induce knowledge transfer to update the voltage control strategy. This allows the proposed strategy to make optimal decisions quickly when only limited data are available under an emergency fault that has never occurred. Comparison results based on a 69-bus distribution network validate the effectiveness and robustness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Traditional energy planning methods are mostly related to newly-built area. However, in most already-built regions, the existing district energy systems (DESs) need to be ameliorated due to insufficient capacity and low efficiency. This paper investigates the methodology of expansion planning, which can offer planners a viable way to expand existing DESs. The term “expansion” aims to increase the supply capacity of the energy forms, e.g., steam, hot water and electricity, which cannot be accessed from national or urban energy supply network, and meanwhile increase energy utilization efficiency and cut CO 2 emission. Three parts of the methodology, including problem analysis in current DESs, demand side analysis and the existing DES expansion, are introduced in this paper. New concepts of load structure and evaluation system of the whole district are proposed in this methodology. A case analysis of Ningbo Hi-Tech District, China is conducted to shed light on the application of this method. [ABSTRACT FROM AUTHOR]

With the proposed concept of energy blockchain, peer-to-peer (P2P) energy trading between prosumers is regarded as a potential way for future power market development. However, existing P2P energy trading often has problems such as low trading efficiency and significant communication losses. This paper presents an energy trading strategy in the blockchain environment to solve the problems of P2P energy trading. First, the type of market is determined based on energy supply and demand. Then, a Stackelberg game is used to establish a two-layer model of the leader (upper) and follower (lower) to determine the price. In addition, we propose an energy trading method based on feasible regions. The trading strategy considers the supply and demand of the market, provides an accurate energy transfer signal, and can help energy blockchain achieve better "self-sufficiency". A case study proves the effectiveness of the strategy. Compared with the existing methods, the strategy proposed in this paper considers the market type and can promote the coordination and complementation of energy in the microgrid, and improve trading efficiency and reduce communication loss without sacrificing privacy. This study demonstrates that P2P power trading has brought benefits to prosumers, has promoted the development of the energy market, and has shown the massive potential of energy blockchain. • A P2P energy trading model and a consensus algorithm in the blockchain are proposed. • An interactive pricing-and-matching P2P energy trading strategy is proposed. • A novel P2P energy trading pricing model based on game theory is proposed. • A P2P energy trading matching method based on the feasible region is proposed. • Results show that the strategy proposed in this paper can optimize the microgrid. [ABSTRACT FROM AUTHOR]

Abstract: In March 2011, China’s central government set a new challenging target of reducing its energy intensity by 16% during 2011–2015, after it had achieved a reduction of 19.1% during 2006–2010. And this new target was assigned to provincial authorities in August 2011. However, China’s provincial energy-economic developments are unbalanced and different provinces have different key sectors for energy conservation. Most previous studies focused on provincial energy efficiency at the aggregate level, or the three-industry level (or one-digit level). However, whether for policy decision or academic research, it is necessary to further subdivide the sectors. In this paper, we use three indicators (Gini Coefficient, energy consumption share and energy intensity) to compare provincial energy conservation potentials at the two-digit sector level. To our knowledge, this paper is the first one to identify the keys for energy conversation across the 31 provinces×65 sectors. And the results are shown in visualized maps and matrix tables to help identify the key province×sectors for energy conservation easier. This also helps the central and provincial governments to distinguish key sectors when they monitor the energy conservation progress. [Copyright &y& Elsevier]

Abstract: HCCI combustion can substantially reduce NOx and soot emissions of diesel engines. However it is hard for diesel HCCI to obtain the same level of fuel economy as traditional diesel engines. This paper presents a way to improve the fuel economy of diesel HCCI by adopting Integrated Starter Generator (ISG). A set of diesel parallel hybrid power system (in this article, we name it “hybrid engine”) is constructed by installing an ISG motor on a diesel engine, which applies HCCI–CI combined combustion. Based on a lot of investigation on diesel HCCI, this paper is focus on improving HCCI engine performance, especially the fuel economy, by adopting ISG motor assist control. The coordination control strategies of engine and ISG motor in HCCI transient states, HCCI–CI transition, engine quick start and braking energy regeneration are developed. The results of equivalent New European Driving Cycle (NEDC) tests show that, by using ISG assistance, the fuel consumption of the diesel HCCI engine is greatly reduced. Meanwhile the NOx and soot emission are also improved. [Copyright &y& Elsevier]

Rapid vehicle and powertrain development has become essential to for the design and implementation of vehicles that meet and exceed the fuel efficiency, cost, and performance targets expected by today’s consumer while keeping pace with reduced development cycle and more frequent product releases. Recently, advances in large-scale additive manufacturing have provided the means to bridge hardware-in-the-loop (HIL) experimentation and preproduction mule chassis evaluation. This paper details the accelerated development of a printed range-extended electric vehicle (REEV) by Oak Ridge National Laboratory, by paralleling hardware-in-the-loop development of the powertrain with rapid chassis prototyping using big area additive manufacturing (BAAM). BAAM’s ability to accelerate the mule vehicle development from computer-aided design to vehicle build is explored. The use of a hardware-in-the-loop laboratory is described as it is applied to the design of a range-extended electric powertrain to be installed in a printed prototype vehicle. The integration of the powertrain and the opportunities and challenges it presents are described in this work. A comparison of offline simulation, HIL and chassis rolls results is presented to validate the development process. Chassis dynamometer results for battery electric and range extender operation are analyzed to show the benefits of the architecture. [ABSTRACT FROM AUTHOR]

Shipping is a major contributor to global CO 2 emissions. Various operational and technical measures have been proposed to reduce ship emissions. However, these emission reduction measures may not be all economically feasible to implement. Therefore, it is important to rank all these measures and select the most cost-effective measures for emissions reduction. Moreover, there are various uncertainties in evaluating emission reduction measures, such as uncertainties of implementation cost, fuel consumption, abatement potential and fuel price. These uncertainties may significantly influence the ranking of the emission reduction measures, which further result in an inappropriate selection of the measures for implementation. In this paper, a ranking algorithm with a new criterion is proposed to rank all the emission reduction measures by considering the preference between cost and abatement. Furthermore, a ranking under uncertainty method is developed which takes into account various uncertainties of the impact factors. This method can support policy makers in ranking and selecting emission reduction measures more appropriately by better quantifying and reflecting the uncertainties. [ABSTRACT FROM AUTHOR]

Integrated Assessment Models (IAMs) are omnipresent in energy policy analysis. Even though IAMs can successfully handle uncertainty pertinent to energy planning problems, they render multiple variables as outputs of the modelling. Therefore, policy makers are faced with multiple energy development scenarios and goals. Specifically, technical, environmental, and economic aspects are represented by multiple criteria, which, in turn, are related to conflicting objectives. Preferences of decision makers need to be taken into account in order to facilitate effective energy planning. Multi-criteria decision making (MCDM) tools are relevant in aggregating diverse information and thus comparing alternative energy planning options. The paper aims at ranking European Union (EU) energy development scenarios based on several IAMs with respect to multiple criteria. By doing so, we account for uncertainty surrounding policy priorities outside the IAM. In order to follow a sustainable approach, the ranking of policy options is based on EU energy policy priorities: energy efficiency improvements, increased use of renewables, reduction in and low mitigations costs of GHG emission. The ranking of scenarios is based on the estimates rendered by the two advanced IAMs relying on different approaches, namely TIAM and WITCH. The data are fed into the three MCDM techniques: the method of weighted aggregated sum/product assessment (WASPAS), the Additive Ratio Assessment (ARAS) method, and technique for order preference by similarity to ideal solution (TOPSIS). As MCDM techniques allow assigning different importance to objectives, a sensitivity analysis is carried out to check the impact of perturbations in weights upon the final ranking. The rankings provided for the scenarios by different MCDM techniques diverge, first of all, due to the underlying assumptions of IAMs. Results of the analysis provide valuable insights in integrated application of both IAMs and MCDM models for developing energy policy scenarios and decision making in energy sector. [ABSTRACT FROM AUTHOR]

This paper explores the impacts of active suspension with energy regeneration capability on the fuel economy of a power split Hybrid Electric Vehicle (HEV). Models of HEV powertrain and suspension are established and, based on their interactions, unified. The impacts of control parameters on the suspension performance are synthetically analyzed. The analyses form a basis for the design of suspension controller. High-level supervisory controller and low-level sub-controllers for HEV powertrain and suspension are designed. Low-level sub-controllers can ensure the performance of each system, whose control parameters are updated by the supervisory controller. The equivalent consumption minimization strategy (ECMS), superior in maintaining charge sustainability, is improved for better application in the proposed system. Simulation results under different conditions demonstrate that both HEV fuel economy and ride comfort are improved. Comparative analyses of various scenarios validate the positive effect of suspension energy regeneration on the energy conservation performance of power split HEV. Meanwhile, the improvement of HEV fuel economy can be highlighted by applying ECMS. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive power management strategy for a novel anti-idling system, regenerative auxiliary power system (RAPS), designed for service vehicles. RAPS is able to utilize recovered braking energy for electrified auxiliary systems; this feature distinguishes it from its counterparts - auxiliary power unit (APU) and auxiliary battery powered unit (ABP). To efficiently operate the RAPS, a power management strategy is required to coordinate power flow between different energy sources. Thus, a model predictive controller (MPC) is developed to improve the overall efficiency of the RAPS. As an optimization-based approach, the MPC-based power management strategy usually requires the drive cycle or the drivers’ command to be known a priori. However, in this study, an average concept based MPC is developed without such knowledge. MPC parameters are tuned over an urban drive cycle; whereas, the robustness of this MPC is tested under different drive cycles (e.g. highway and combined). Analysis shows that, the presented MPC has a comparable performance as the prescient MPC regarding fuel consumption, which assumes knows the drive cycle beforehand. Meanwhile, with the help of the proposed MPC and RAPS, the service vehicle saves up to 9% of the total fuel consumption. The proposed MPC is independent of powertrain topology such that it can be directly extended to other types of hybrid electric vehicles (HEVs), and it provides a way to apply the MPC even though future driving information is unavailable. [ABSTRACT FROM AUTHOR]

With the increasing negative effects of fossil fuel combustion, many countries have paid more attention on supporting environmentally friendly energy generation, particularly renewable and sustainable energy sources (RES). However, renewables still cannot economically compete with fossil-fuels and are facing new challenges. Appropriate electricity pricing mechanism plays a vital role on mitigating the stress of limited fossil fuels and can promote renewable energy consumption. As one major type of renewable energy, wind power has been globally promoted. In order to promote the application of wind power, appropriate wind power prices (WPP) should be established by considering the local conditions. This paper targets such a field and conducts a bibliometric and network analysis based on the data from Scopus. The results show that the numbers of total related publications are gradually increasing, with the US as the leading country. European countries also have outstanding achievements. Moreover, both the most cited articles and keywords distribution offer future research directions. In general, this study provides valuable insights to both wind power researchers and practitioners. [ABSTRACT FROM AUTHOR]

In this paper a data-validated power-efficiency model of a diesel-powered fuel-cell-based auxiliary power unit (APU) system is used to investigate the various sizes of the power unit and the battery and to evaluate the optimal choices for specified load profiles. The challenge comes from the FCGEN (Fuel Cell-based power GENeration) EU FP7 project, where such an APU was developed. The system consists of a fuel processor, a PEM stack, and a battery providing power for the startup, shutdown, and for covering load transients; however, the developed prototype system is not optimised. Before redesigning it for mass production, the optimal size of the main components needs to be identified to enable the best possible exploitation of the technology. In this work a case-specific load profile was used and a mesh grid of scenario simulations has been performed using various sizes of the fuel cell with fuel processor as a power unit and the batteries of various capacities as an energy storage unit. For this purpose a scalable APU model, including the BoP component consumption, has been developed. Upon the analysis results, the relation for optimal combinations in terms of efficiency and degradation is proposed and the confronted tradeoffs are discussed. [ABSTRACT FROM AUTHOR]

The objective of this paper is to systematically identify and assess critical factors, which foster or hinder the development of bottom-up initiatives in the diffusion of photovoltaics. Bottom-up initiatives are social innovations, which entail civil engagement in energy transition at a local or regional level, and are expected to play a growing role in the governance of local energy systems in Europe. A mixed design methodology is used to identify critical factors and assess their importance. This involves combining an analysis of strengths, weaknesses, opportunities and threats with an analytic hierarchy process. The findings indicate that successful initiatives are those which are able to draw upon substantial local public interest and trust in the new technology, and which manage to combine financial attractiveness with environmental concerns. The results make clear that the political context is also an extremely important success factor. Given the appropriate circumstances, such initiatives may make a significant contribution in the transition to a sustainable energy system, and thus prove useful in reaching European energy targets. [ABSTRACT FROM AUTHOR]

Energy sectors of all the countries around the world are faced with a big challenge: the energy transition under the imperative of continuous reduction of carbon dioxide emissions. The energy transition and the sustainability constraints pose a significant challenge for engineers, researcher and scientists developing new and clean energy-efficient technologies. Energy production and transformations, along with its storage, distribution and consumption, are achieved by utilising new and clean energy technologies, with the goal of the continuous increase of energy efficiency, the growth of renewable energy sources utilisation, the uninterrupted switch from fossil fuels to alternative energy sources, and the continued reduction of CO2 emissions. This editorial paper addresses the latest progress and the findings in research and developments in the sustainable energy technologies, energy and environment systems from selected papers of the conference on Sustainable Development of Energy, Water and Environment Systems held in 2019. [ABSTRACT FROM AUTHOR]

In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy ( E RDE ) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available E RDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the E RDE directly to the current state of charge (SOC). To enhance the E RDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the E RDE prediction horizon, and the E RDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different E RDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the E RDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online E RDE prediction. The correlation of SOC estimation and E RDE calculation is then discussed to illustrate the importance of an accurate E RDE method in real-world applications. [ABSTRACT FROM AUTHOR]

Environmental performance (EP) uncertainties span a number of energy technology options, and pose planning risk when the energy system is subject to environmental constraints. This paper presents two approaches to integrating EP uncertainty into the long-term energy planning framework. The methodologies consider stochastic EP metrics across multiple energy technology options, and produce a development strategy that hedges against the risk of exceeding environmental targets. Both methods are compared within a case study of emission-constrained electricity generation planning in British Columbia, Canada. The analysis provides important insight into model formulation and the interactions with concurrent environmental policy uncertainties. EP risk is found to be particularly important in situations where environmental constraints become increasingly stringent. Model results indicate allocation of a modest risk premium in these situations can provide valuable hedging against EP risk. [ABSTRACT FROM AUTHOR]

[Display omitted] • A directly solar-driven power-to-methane system is proposed. • The detailed distributed parameter method is used to model the system. • A comprehensive thermodynamic and economic analysis is performed. • Energy and exergy efficiencies are 9.88% and 11.08% with 914.51 MWh/y SNG yield. • Unit cost is 186.57 $/MWh in 2020 and will be reduced to 91.51 $/MWh in 2050. The transformation of solar energy into easily transportable and storable fuel brings a prospective solution to the issue caused by its intermittency and non-dispatching. Relying on well-established infrastructures and mature technologies for application, methane can be an attractive energy carrier for improving the penetration of solar energy in the future energy structure. To evaluate the feasibility of solar-to-methane from technical and economic views, this paper proposes a directly solar-driven power-to-methane system integrating photovoltaic plant, solid oxide electrolysis cell, methanation reactor, and membrane module. The detailed distributed parameter method is employed for thermodynamically modeling the core components of the system to consider the profiles of current density, temperatures, and composition concentrations. A comprehensive thermodynamic and economic analysis is further conducted for the system, and the results indicate that the power-to-methane efficiency, total energy efficiency, and total exergy efficiency of the system can reach 68.41%, 9.88%, and 11.08%, respectively. With the annual average solar radiation of 557.43 W/m2, the system achieves an annual synthetic natural gas yield of 914.51 MWh/y. A high total product unit cost of 186.57 $/MWh is obtained in 2020, whereas it is predicted to decrease to 91.51 $/MWh (about 51% reduction) in a future scenario with a payback period of 4.66 years due to the significant decrement of the photovoltaic investment cost. The present work reveals the engineering realizability and economic viability of solar-to-methane and provides a competitive option for the development of solar energy. [ABSTRACT FROM AUTHOR]

• Spatial pattern and evolution trends of four kinds of residential energy consumption in China are investigated. • Spatial difference in the distribution of gravity centers of residential energy consumption is depicted. • Standard deviational ellipse demonstrates the dynamic evolution patterns of residential energy consumption. • Coupling coordination degree between residential energy consumption and the economic indicator is investigated. • Electricity should be taken into further consideration in the development of residential energy improvement. The residential energy consumption in China shows diversity and complexity in space. This paper aims to investigate the spatial pattern and evolution trends of four kinds of residential energy consumption (electricity, coal, oil, and natural gas) through gravity center modeling and standard deviational ellipse modeling. Further, their relations to economic growth are probed by using the coupling and coordination degree model. Panel data at the provincial level in China are collected from 2000 to 2019. Our results show that the most drastic movement trend of gravity centers is coal, natural gas, followed by oil, whereas electricity moved slightly in space. Further, the standard deviational ellipse of electricity exhibits relatively stable patterns than the other three residential energy consumption during the study period. In addition, the coupling coordination degree between residential energy consumption and the economic growth is examined, and the results indicate that the total residential electricity consumption shows the highest coupling coordination degree, followed by residential gas, oil, and coal. It demonstrates lower coupling coordination degrees in rural areas than in urban areas in the total and sectoral residential energy consumption. In sum, several policy implications can be demonstrated based on our findings, i.e., the development of electricity consumption in the residential energy structure may improve the energy stability in space in China, and ensuring access to electricity should be taken into further consideration in the development of residential energy improvement to achieve a better coordination level to economic development. [ABSTRACT FROM AUTHOR]

• The total-factor carbon performance (TFCP) of 72 economies during 2000–2018 is calculated to indicate low carbon development. • The mediation analysis and the moderation analysis are conducted, and the robustness of the results is checked by the Sobel Test and the Bootstrap Test. • Clean energy use is the mediator of the positive impact of national affluence and R&D on TFCP. • Clean energy use mitigates the negative impact of industrialization on TFCP. The world seeks for a more sustainable and low carbon development (LCD) and the application of clean energy forms a critical solution in this process. However, there are few studies about how clean energy utilization works as a mediator between national income level and LCD as well as between R&D and LCD or a moderator adjusting carbon emission activities in the world's low-carbon development. We hypothesize that clean energy use is a mediating variable for national income levels and R&D investment and positively affect low-carbon development. In contrast, clean energy acts as a moderating variable that mitigates the negative impact of industrialization on low-carbon development. To test the hypothesis, this study uses panel data of 72 economies from 2000 to 2019. Total-factor carbon performance (TFCP) is calculated by fixed-effects stochastic frontier analysis and indicates LCD level. The hypothesis is supported by the results of mediation and moderation analysis. The Sobel mediation test and Bootstrap Test are applied to verify the robustness of the results. This paper deeply reveals the importance of clean energy utilization as a mediator and a moderator. Under the pressure of industrialization and increasing energy need for economic growth, clean energy should have a larger share. Investments and R&D should pay more attention to clean energy development. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper presents a novel methodology for modeling building energy performance. [•] The novelty lies in the use of lead-lag dynamics to present building energy system. [•] The developed model is simple and accurate with minimal physical parameters. [•] Comparison with benchmarks and measurements shows good results. [Copyright &y& Elsevier]

Abstract: Development and deployment of low-carbon energy technologies has been a national strategy of both the UK and China for a number of years, including the use of renewable generation technologies and the improvement of energy efficiency of operations and activities. The paper addresses several issues of generic importance to a residential microgrid system such as network modelling, advanced control and integration of intelligent monitoring techniques. The system, comprising representative distributed generation technologies of photovoltaics, wind turbines and combined heat and power, has been simulated by PSCAD/EMTDC under different operational scenarios. Studies include the effect of environmental condition changes, control systems and power electronics on wind turbines and PV cells, and the mixture of wind/solar/CHP energy generation under dominance of each technology. The performance and dynamics of the system are examined against symmetrical and asymmetrical electrical faults to seek an optimal isolation and restoration of the distributed generation unit from the connected grid system. Modelling these system interactions has demonstrated the feasibility of the proposed residential microgrid system. [Copyright &y& Elsevier]

Abstract: As a representative of many emerging economies, BRICS’ economies have been greatly developed in recent years. Meanwhile, the proportion of energy consumption of BRICS to the whole world consumption has increased. Therefore, it is significant to analyze and compare the energy efficiency among them. This paper firstly utilizes a Super-SBM model to measure and calculate the energy efficiency of BRICS, then analyzes their present status and development trend. Further, Bootstrap is applied to modify the values based on DEA derived from small sample data, and finally the relationship between energy efficiency and carbon emissions is measured. Results show that energy efficiency of BRICS as a whole is low but has a quickly increasing trend. Also, the relationship between energy efficiency and carbon emissions vary from country to country because of their different energy structures. The governments of BRICS should make some relevant energy policies according to their own conditions. [Copyright &y& Elsevier]

Abstract: An innovative idea on extracting clean energy from man-made wind resources with micro wind turbine system for power generation is introduced in this paper. This system generates on-site clean energy using a micro wind generation system. A vertical axis wind turbine (VAWT) with an enclosure is mounted above a cooling tower’s exhaust fan to harness the wind energy for producing electricity. The VAWT is positioned at a specific position at the cooling tower outlet to avoid a negative impact on the performance of the cooling tower. The enclosure can act as a safety cover and also enhance the performance of the VAWT. It is designed with several guide-vanes positioned at the up-stream side of the wind turbine to create a venturi effect and guide the wind before it interacts with the turbine blades. Moreover, the enclosure design is comprised of diffuser-plates that can draw more wind and accelerate the flow. Laboratory test conducted on a scaled model shows no measurable difference in the air intake speed and current consumption of the power-driven fan when the turbine was spinning above the cooling tower. Field test on an actual induced-draft cooling tower shows no significant difference on the outlet air speed of the cooling tower. A small difference was observed on the power consumption by the fan motor which is 0.39% higher with the presence of the VAWT. This system is retrofit-able to existing cooling towers and has very high market potential due to abundant cooling towers and other unnatural exhaust air resources globally. [Copyright &y& Elsevier]

• It summarizes the building integrated solar concentrating systems according to different functions. • It introduces the demands, types, applications and structures of the building integrated solar concentrating systems. • It presents prospects/directions/policies around the world towards the building integrated solar concentrating systems. In the building sector, concerns towards the vast energy consumption has promoted the development of renewable energy technologies. In this regards, the solar concentration devices show a promising concept for building applications. However, the solar concentrators for application in buildings have many restrictions, which are different from the traditional solar concentrators. The main objective of this paper is to present a concise review on the building integrated concentrating devices, that have their own characteristics and multiple functions. This paper made a classification based on device's functions, i.e. building integrated concentrated photovoltaic systems (BICPV), building integrated concentrating solar thermal (BICST) and building integrated concentrating solar daylighting (BICSD) and the combination of functions, i.e. BICPV/T, BICPV/D, BICST/D and BICPV/T/D. At the same time, this paper presented an elaborate introduction of the demands, types and applications of the building integrated concentrating devices and prospects/ directions/ policies about these technologies around the world. The review would provide important information for the actual engineering of building integrated concentrating devices. [ABSTRACT FROM AUTHOR]

• Discussed cross-building energy prediction in a limited-data context. • Compared different methods in a same case via a competition. • Identified hybrid strategies for hybrid building energy prediction models. • Discussed the shortcomings and suggestions for the data preparation process. • Highlighted the importance of data selection for accurate cross-building prediction. With the evolution of new energy and carbon trading systems, it is important to accurately predict building energy consumption to help energy arrangements. Additionally, the widespread use of smart meters has introduced a new data context for building energy prediction. Building energy prediction techniques need improvement but the ideas of various new prediction methods are still on the way and have not yet been compared and tested side-by-side in the reported studies. Thus, we held a competition called 'Energy Detective'. To investigate the status quo of the current prediction techniques, we designed a representative prediction case: cross-building prediction with limited physical parameters and historical data. A total of 195 participants formed 89 teams to participate in the competition. This paper describes the models presented in the competition. By analysing the methods and results, we identified strategies for the future development of energy prediction in hybrid modelling and data-driven modelling. For hybrid modelling, we discuss the basic strategies for hybrid models and suggest that more hybrid models can be developed by combining a wide variety of individual models in sequence or parallel or via feedback methods to achieve accurate and interpretable models. For data-driven modelling, we analyse and discuss the areas of improvement for the current data-driven workflow and suggest that processes other than model application are also important and should be carefully considered. Considering the increasing amount of data available for prediction, we discuss the shortcomings and suggestions for improving the current data preparation process. We recommend comprehensive consideration of the anomaly types in data pre-processing and a focus on feature engineering for higher accuracy and model interpretability, while emphasising the vital role of data selection in cross-building energy prediction. [ABSTRACT FROM AUTHOR]

Abstract: Cogeneration is commonly recognized as one of the most effective solutions to achieve the increasingly stringent requirements in primary energy consumption reduction and greenhouse emissions reduction. The potential of cogeneration led to the adoption of specific directives promoting this technique. In addition, distributed generation plays a strategic role in power reliability. The study and prototyping of cogeneration plants has thus involved many research centers. Similar activities were carried out by DiME (University of Naples). These activities highlighted the need to study the cogeneration system-user interaction to estimate the real energetic and economic benefits. The paper develops a specific methodology that is used to conduct an analysis on the loads of a specific hospital facility. The energetic and economic benefits generated by CHP plants depend on plant and user characteristics, plant layout, management strategies, and economic variables. Therefore, the potential benefits that have attracted the attention of the scientific community are not always granted. For this reason, a predictive analysis is needed to find the optimum configuration of the plant (i.e., engine size, plant configuration, management strategies, absorption chiller size, engine number) that approaches the best energetic solution while ensuring a reasonable profit. Therefore, this study attempts to determine the optimal configuration through a multi-objective approach. [Copyright &y& Elsevier]

Abstract: Cooling systems consume up to 25% of the total electricity used on deep level mines. These systems are integrated with the water reticulation system to provide chilled service water to the mine as well as cooling for mine ventilation air. Although there is definite potential for demand-side management on these systems, it is critical that the service delivery be maintained so as not to adversely affect productivity. An energy saving strategy based on variable water flow was developed for the unique demands of integrated mine cooling systems. The strategy is based on matching the evaporators with the demand of chilled water; condensers adapting to the heat load; and the bulk air cooler matching the demand of ventilation air requirements. In this paper, a case study is presented in which the savings and consequences of implementing the developed energy saving strategy are investigated. It is shown that a decrease of 31.5% in overall electrical energy usage is possible without affecting the service delivery or performance of the cooling system. [Copyright &y& Elsevier]

Abstract: In this paper, a gas engine system capable of stable operation at any mix ratio of city gas 13A and biogas was developed. The gas engine system consists of a spark-ignition gas engine, an additional electric throttle valve for fuel and our own control algorithm. The engine is a 3-cylinder 1.6-l engine that was originally used for co-generation, and the fuel throttle valve was added to respond to different fuel compositions. The control algorithm was also designed to adjust the fuel and air ratio to attain a higher generation efficiency and lower NOx emission with different mix ratios of city gas 13A, biogas and load. Before developing the controller, the effect of the mix ratio on generation efficiency and NOx emission was investigated under various load conditions. The following summarizes the experimental results: a control algorithm using the Wobbe index for mixed fuels was formulated; this index determines the target fuel-to-air ratio. Next, operation tests were performed under varying fuel mix ratios and loads by applying the control algorithm to the gas engine. The target engine rotational speed and exhaust O2 concentration was realized in 5s when the biogas fraction varied from 20% to 40% and from 70% to 40%. When the load was also varied from 9.4kW to 0.5kW and from 0.5kW to 9.4kW at a constant rate, the rotational speed and exhaust O2 concentration achieved the target values in 20s. Under both transient operation conditions, the engine system met the NOx emission requirement, and the results indicate that the simple hardware modification to a conventional gas engine and our original control algorithm are capable of achieving stable engine operation at any mix ratio of city gas 13A and biogas. [Copyright &y& Elsevier]

Abstract: This paper develops new real time prediction models for output power and energy efficiency of solar photovoltaic (PV) systems. These models were validated using measured data of a grid-connected solar PV system in Macau. Both time frames based on yearly average and monthly average are considered. It is shown that the prediction model for the yearly/monthly average of the minutely output power fits the measured data very well with high value of R 2. The online prediction model for system efficiency is based on the ratio of the predicted output power to the predicted solar irradiance. This ratio model is shown to be able to fit the intermediate phase (9am to 4pm) very well but not accurate for the growth and decay phases where the system efficiency is near zero. However, it can still serve as a useful purpose for practitioners as most PV systems work in the most efficient manner over this period. It is shown that the maximum monthly average minutely efficiency varies over a small range of 10.81% to 12.63% in different months with slightly higher efficiency in winter months. [Copyright &y& Elsevier]

With the rapid development of wind energy utilization, people pay more and more attention to improving the accuracy of wind speed prediction. However, the unstable and nonlinear nature of wind speed makes it difficult to predict accurately. Due to the accumulation of prediction errors, the prediction steps in many studies are very short. In this paper, a novel super multi-step forecasting system with structure of "data extraction - horizontal multi-output prediction - vertical integration prediction" is constructed to make 7-day, 15-day and 30-day wind speed prediction without updating the data source. A new mutual information matrix is designed to select the decomposition number of the singular spectrum analysis. Two different types of bidirectional long short-term memory network are constructed to realize horizontal–vertical integration prediction. A new training set extension method is applied to improve the generalization ability of the neural network. Three wind speed data sources and five comparison models are used to evaluate the performance of the proposed system. The empirical results illustrate that: (a) the maximum mean absolute errors of prediction results of three sites are 1.371 m/s, 0.567 m/s and 0.580 m/s, respectively, which shows the model has better prediction performance; (b) the mean absolute errors of the three sites increased by -0.050 m/s, 0.079 m/s and 0.113 m/s, respectively, which shows the proposed system has no obvious accumulative errors; (c) the proposed forecasting system shows better performance than the comparison models. [Display omitted] • Multi-output prediction method without updating data and accumulative errors. • Model with horizontal–vertical integration bidirectional neural network. • New training set extension method based on random number addition. • New decomposition number selection method based on mutual information matrix. • New similar segment comparison method to show the predictability of data. [ABSTRACT FROM AUTHOR]

Energy resources in outer space, also known as space energy, has been recognized as a promising supplement to conventional energy supplies on Earth, as well as an irreplaceable energy provision for future space explorations. A critical review is conducted in this paper, to identify the most potential space energy resources, to conclude on the current exploitation technologies and to suggest on the challenges and future directions. Space solar power station, also known as SSPS, is presented first as a well-known utilization of space energy, and we go through the international progress, evolution of the collection systems and the thermophotovoltaic systems. The main technical gaps hampering the practical application of SSPS is concluded then to inspire future investigations. Energy on Mars is presented afterwards as a representative ISRU(In Situ Resource Utilization)-type energy resource, and we select three potential resources on Mars worth exploitation: solar energy, geothermal energy and wind energy. A model describing the global solar irradiance on Mars is concluded, typical applications of geothermal energy is analyzed, the phase equilibrium of geothermal fluids is established and the wind turbine is designed. Furthermore, the review on energy on Moon is started with the discussion on lunar geology relevant with energy resources, and an example of feature detection using Convolutional Neural Networks is illustrated as an example to demonstrate the application of deep learning techniques in space energy exploitation. Solar energy is always taken into account in space activities, and we are more focusing on the discussion of Helium-3, a promising resource for nuclear fusion. The material for nuclear fission, Uranium, has also been detected on Moon. A summary is provided in the end with concluding remarks. • A comprehensive review on space energy, with a thorough investigation on literature and international news. • Declaration of three main space energy utilization manners and information provided on the representative resources. • Study on the application of well-developed conventional energy development technologies on space energy utilizations. • Technic details on resource recognition, exploitation facilities and mechanism analysis. [ABSTRACT FROM AUTHOR]

• A practical and effective method is presented for energy prediction of machine tool. • Energy consumption features are extracted by using unsupervised deep learning. • Supervised deep learning is used to develop energy prediction model of machine tool. • This method is a generalized way for identifying energy of different machine tools. • The results show that the method could improve the energy prediction performance. Energy prediction of machine tools plays an irreplaceable role in energy planning, management, and conservation in the manufacturing industry. In the era of big machinery data, data-driven energy prediction models of machine tools have achieved remarkable results in the identification of energy consumption patterns and prediction of energy consumption conditions. However, existing data-driven studies towards the energy consumption of machine tools focus on the utilization of handcrafted-feature learning methods, which are inefficient and exhibit poor generalization. Moreover, considering variations in energy consumption characteristics among different machine tools, it is impractical to identify energy consumption features manually for energy model development. Therefore, this paper proposes a novel data-driven energy prediction approach using deep learning algorithms. Here, deep learning is used in an unsupervised manner to extract sensitive energy consumption features from raw machinery data, and in a supervised manner to develop the prediction model between the extracted features and the energy consumption of machine tools. The experiments conducted on a milling machine and a grinding machine are exploited and compared with those conducted in conventional studies. The results show that the proposed method can improve the energy prediction performance from 19.14% to 74.13% for the grinding machine and from 64.89% to 85.61% for the milling machine, and it achieves a better performance than the conventional methods in terms of effectiveness and generalization. [ABSTRACT FROM AUTHOR]