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The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. The commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

Abstract Reversible carbonation/calcination of CaCO3 is a promising technology for thermochemical energy storage in concentrated solar power plants. However, the major drawback of this technology is the rapid deactivation of CaO due to sintering. In this study, newly developed CaCO3/graphite nanosheets composites were synthesized as the heat storage medium through a one-pot route varying the graphite nanosheets load (3–12 wt%). The impregnation of the composites with H3BO3 solutions enhance the initial weightlessness temperature of graphite nanosheets from 900 °C to 1050 °C in pure CO2 atmosphere, thus upgrading the stability of graphite nanosheets during heat storage/release process. The performances of the synthesized composites were evaluated by thermogravimetric analysis, which simulates the heat storage cycle. The composites showed improved heat storage/output capacity and faster heat input/output rate compared to pure CaCO3. Only 3 wt% of graphite nanosheets was needed to effectively stabilize the heat storage capacity of the material. The heat storage capacity of composites with 3 wt% graphite nanosheets is 1313 kJ/kgcomposite after 50 cycles, corresponding to 2.9 times as much as that of pure CaCO3. This high stability is attributed to the unique synthetic strategy in which the CaCO3 nanoparticles uniformly coated on graphite nanosheets surface, and their sintering and aggregation were prevented. This work brings the development of this technology to a level closer to its industrial application.

Abstract In an effort to minimise electricity consumption and greenhouse gases emissions, the heating, ventilation and air-conditioning sector has focused its attention on developing alternative solutions to electrically-driven vapour-compression cooling. Liquid desiccant air-conditioning systems represent an energy-efficient and more environmentally friendly alternative technology for dehumidification and cooling, particularly in those cases with high latent loads to maintain indoor air quality and comfort conditions. This technology is considered particularly efficient in hot and humid climates. As a matter of fact, the choice of the desiccant solution influences the overall performance of the system. The current paper reviews the working principle of liquid desiccant systems, focusing on the thermodynamic properties of the desiccant solutions and describes an evaluation of the reference thermodynamic properties of different desiccant solutions to identify which thermodynamic, physical, transport property influences the liquid desiccant process and to what extent. The comparison of these thermodynamic properties for the commonly used desiccants is conducted to estimate which fluid could perform most favourably in the system. The economic factors and the effect of different applications and climatic conditions on the system performance are also described. The paper is intended to be the first step in the evaluation of alternative desiccant fluids able to overcome the problems related to the use of the common desiccant solutions, such as crystallization and corrosion to metals. Ionic liquids seem a promising alternative working fluid in liquid desiccant air-conditioning systems and their characteristics and cost are discussed.

Thermal comfort is an important concern in the energy efficiency improvement of commercial buildings. Thermal comfort research focuses mostly on temperature control, but humidity control is an important aspect to maintain indoor comfort too. In this paper, an optimal humidity control model (OHCM) is presented. Model predictive control (MPC) strategy is applied to implement the optimal operation of the desiccant wheel during working hours of a commercial building. The OHCM is revised to apply the MPC strategy. A case is studied to illustrate the practical applications of the MPC strategy.

Shale gas, due to its clean-burning and efficient nature, is becoming an increasingly promising alternative energy resource. It is commonly held that promoting shale gas development will gradually play a significant role in meeting the energy needs of economic and social development as well as reducing harm to the environment. Given the significant implications, many countries are pursuing shale gas opportunities. However, numerous concerns have been raised about the economics of shale gas development, as it is difficult to evaluate. Accurately evaluating the economic viability of shale gas development to reduce investment risks and increase investment opportunity is the key issue that needs to be urgently addressed. This paper presents a systematic review and examination of the technical and economic evaluation techniques for the development of shale gas to provide an overview of their current status. Over time, some progress has been made in existing technical–economic evaluation techniques. It is worth noting that these techniques need to be further improved to more precisely assess the economic feasibility of developing shale gas for assisting investment decisions effectively. For this reason, various potentially useful ideas and approaches are presented to propose some potential improvement in evaluation techniques for shale gas development, which may materialize in possible future trends.

Abstract In solid fuel (such as lignite) chemical looping combustion, solid fuels undergo pyrolysis and gasification. The volatiles from pyrolysis and the gasification product (CO/H 2 ) react with oxygen carriers. The gas conversion (to CO 2 /H 2 O) in the fuel reactor is a key point. However, char particles of different sizes and conversion ratios cause segregation in the fuel reactor, which influences the contact time between fuel gases and the carrier, thereby changing the gas conversion behavior. In order to gain information on obtaining a high gas conversion in the fuel reactor, this work focused on the effect of the char particle segregation on gas conversion. Different factors – the char particle size, the fluidizing gas velocity, and the oxygen carrier reactivity – were taken into account. Smaller char particles with low density would float on top of the fluidized bed, corresponding to a low gas conversion ( U mf in the Fe63Al bed) can reduce the segregation effect, resulting in a higher CO conversion. High reactivity carriers can convert CO completely although segregation exists, whereas low reactivity carriers exhibit the segregation effect and thus corresponds to a low CO conversion.

Abstract With increasing prosumers employed with flexible resources, advanced demand-side management has become of great importance. To this end, integrating demand-side flexible resources into electricity markets is a significant trend for smart energy systems. The continuous double auction (CDA) market is viewed as a promising P2P (peer to peer) market mechanism to enable interactions among demand side prosumers and consumers in distribution grids. To achieve optimal operations and maximize profits, prosumers in the electricity market must act as price makers to simultaneously optimize their operations and trading strategies. However, the CDA-based market is difficult to model explicitly because of its information-based clearing mechanism and the stochastic bidding behaviors of its participants. To facilitate prosumers actively participating in the CDA market, this paper proposes a novel prediction-integration strategy optimization (PISO) model. A surrogate market prediction model based on Extreme Learning Machine (ELM) is developed, which learns the interaction relationship between prosumer bidding actions and market responses from historical transaction data. Moreover, the prediction model can be conveniently transformed and integrated into the prosumer operation optimization model in the form of constraints. Therefore, prosumer operations and market trading strategies can be jointly optimized through the proposed approach, facilitating the integration of flexible resources into electricity markets. Numerical studies demonstrate the effectiveness of the proposed model by comparing with existing CDA trading strategies under various market conditions.

Abstract Urban planners, local authorities, and energy policymakers often develop strategic sustainable energy plans for the urban building stock in order to minimize overall energy consumption and emissions. Planning at such scales could be informed by building stock modeling using existing building data and Geographic Information System-based mapping. However, implementing these processes involves several issues, namely, data availability, data inconsistency, data scalability, data integration, geocoding, and data privacy. This research addresses the aforementioned information challenges by proposing a generalized integrated methodology that implements bottom-up, data-driven, and spatial modeling approaches for multi-scale Geographic Information System mapping of building energy modeling. This study uses the Irish building stock to map building energy performance at multiple scales. The generalized data-driven methodology uses approximately 650,000 Irish Energy Performance Certificates buildings data to predict more than 2 million buildings’ energy performance. In this case, the approach delivers a prediction accuracy of 88% using deep learning algorithms. These prediction results are then used for spatial modeling at multiple scales from the individual building level to a national level. Furthermore, these maps are coupled with available spatial resources (social, economic, or environmental data) for energy planning, analysis, and support decision-making. The modeling results identify clusters of buildings that have a significant potential for energy savings within any specific region. Geographic Information System-based modeling aids stakeholders in identifying priority areas for implementing energy efficiency measures. Furthermore, the stakeholders could target local communities for retrofit campaigns, which would enhance the implementation of sustainable energy policy decisions.

Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics is based on the analysis of sequences of ground-taken sky images or satellite observations. Despite encouraging results, a recurrent limitation of existing deep learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a frequent temporal lag and limited ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture that models cloud motion from sky images to not only predict future irradiance levels and associated uncertainties, but also segmented images, which provide richer information on the local irradiance map. We show that ECLIPSE anticipates critical events and reduces temporal delay while generating visually realistic futures. The model characteristics and properties are investigated with an ablation study and a comparative study on the benefits and different ways to integrate auxiliary data into the modelling. The model predictions are also interpreted through an analysis of the principal spatio-temporal components learned during network training. Manuscript accepted for publication in Applied Energy

Abstract In the past, most studies considered solar as a drawback to road pavement, especially the asphalt pavement. Not only its radiation causes the asphalt aging, simultaneously with high temperature, asphalt pavement contributes to the urban heat island (UHI) effect. In recent, as the world becomes more supportive towards implementing sustainable energy as the alternatives to the scarcity of non-renewable resources, utilizing the abundant solar radiation as the main source of energy has gradually attracted attention from both industries and academia with its potentials, including solar application on roads. Nevertheless, more researches have focused on the potential energy collection with less consideration to simultaneously overcome the side effects of abundant solar radiation on its lifecycle. This study found the necessity to review a comprehensive correlation between solar radiation and asphalt pavement from both positive and negative effects. The negative effects included asphalt aging and its effects on surroundings i.e. UHI effect. Meanwhile, the positive effects included exhaust purification and solar energy conversion into other forms of energy that can be used by humans using pavement solar heat collection for winter snowmelt, thermoelectric technology and photoelectric technology to convert solar energy into electrical energy. The cases in various application scenarios were analysed and summarized and the existing problems in the current research were proposed in this paper. Also, the reflection on the transformation of road design concept from “avoiding harm” to “seeking profit” was included. Finally, a new concept of “solar-road harmonious symbiosis” for future road application was proposed based on a comprehensive review of previous published works. It is also the new suggestion to scholars on the treatment of solar diseases on roads to prolong its symbiosis functions.