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The widely used coal-fired boilers are polluted and inefficient, the single-stage air source compression or absorption heat pumps suffer from poor applicability in low temperature. In order to overcome the above shortcomings and achieve efficient heating in cold regions, the novel air source hybrid heating system driven by off-peak electric thermal storage is proposed. In this work, the thermodynamic models of the hybrid system are developed and validated. Using these models, the energy and economic advantages of the system in the research cities can be determined by formulating rational operation strategies. The results show compared with reference heating systems, the coefficient of performance of the proposed system can be increased by 62%∼125%, and the heating cost can be reduced by 37.30%∼55.48% when applied in Shenyang. When the ratio of centralized heating price to off-peak electricity price is less than 51.2 kW h/m2, 54.9 kW h/m2, 76.9 kW h/m2, the economic advantage of the hybrid system over centralized heating will disappear in Beijing, Shenyang, and Harbin, respectively. Only when the ratio of natural gas price to off-peak electricity price is greater than 6.0 kW h/m3, 6.2 kW h/m3, 6.7 kW h/m3 in the above three cities respectively, the economic advantage over distributed gas-fired heating can be reflected. [ABSTRACT FROM AUTHOR]

Highlights • Neural network algorithms can predict electricity consumptions in buildings. • The proposed algorithms can be used effectively for energy consumption prediction. • The LM-BP algorithm is more accurate and stable than the BP neural network. • The neural network has a powerful learning function based on input parameters. Abstract This study considered combinational control approaches for predicting electricity consumption in a building based on an optimized back-propagation (BP) neural network. Forecasting the electricity consumption of buildings is an important part of power management systems because it directly affects power dispatching and safe production. The BP neural network prediction method is used widely for this purpose because of its high plasticity and simple structure, but it has disadvantages such as slow convergence, fluctuations, and oscillation during training. We propose the use of the Levenberg–Marquardt back-propagation (LM-BP) neural network to enhance the accuracy of predictions by combining the gradient descent and Quasi-Newton method, thereby ensuring the fast convergence speed and maintaining better overall performance. Additionally, the network weights can be optimized by adaptive adjustment between the steepest gradient descent method and the Gauss–Newton method so the network can converge effectively. Hence, the accuracy and stability of the LM algorithm are improved, and a building electricity consumption prediction model can be established based on the improved BP neural network. Our results demonstrate that the forecasting model based on LM-BP neural network improves the accuracy and stability of predictions, and it is suitable for the short-term prediction of building electricity consumption. [ABSTRACT FROM AUTHOR]

• PV potential estimation in sensitive areas. • Involvement of Public and Heritage authority in the decision-making process. • Respect of original morphology, typology, material, and historic* features. • Stochastic models to consider occupants' behavior, and cultural diversity. • PV potential index for heritage-compatibility in historic* towns. The study aims at defining a methodology for estimating the photovoltaic potential of natural and heritage sensitive land areas, balancing heritage protection and energy production thanks to the involvement of Public and Heritage Authorities. This method is applied to the Como Land Area (Italy) for verify its feasibility in territories with heterogeneous morphologies, landscapes, and constraints. First, " urban area typology " and their " recurring buildings typologies " are identified using local sources and tools with the support of the Public Authorities for considering similar shapes, features, materials, roof geometries, and orientations as well as future policy developments. Then, heritage-compatible photovoltaic technologies and suitable surfaces are selected for one " recurring buildings typology " respecting original appearance, values, and meanings thanks to the enquires with the local Heritage Authority. Considering these recommendations, the photovoltaic potential in covering energy and building needs is estimated considering the impact of urban geometries, self-shadings, heritage and territorial constraints. Energy profiles are generated using stochastic models calibrated to include occupant diversity and cultural factors. An average index for the photovoltaic potential reduction due to the heritage-compatibility is found respectively of 21% and 16% compared with the maximum theoretical potential achievable in the building envelope and in the roof. [ABSTRACT FROM AUTHOR]

Looking at modern options of energy conservation in buildings it is necessary to assure energy efficiency first. When the energy load of a building is highly decreased, then innovative options of energy conservation can be introduced. The background and development of the main modern methods for the reduction of energy needs, final and primary energy consumption are analyzed. Differentiation between options for existing and newly constructed buildings is pointed out. Modern options of energy conservation in buildings are focused on innovative architecture, shape, structure, materials of a building and of course systems utilizing renewable energy. A very simple way of reducing building energy needs is the utilization of solar energy in a passive, but planned way. A specific shape of solar buffer space should be created in the building. In high latitude countries the buffer space should be of a specific design containing two cuboid sub-spaces with specific internal overhang and a well-planned extension of the south glazed facade. The paper presents modern renewable energy technologies as technologies with roots in past ideas of using the environment in an effective way. It underlines that future innovative and efficient building technologies will use building integrated renewable, mainly solar technologies. [ABSTRACT FROM AUTHOR]

In the current energy scenario, system design and operation strategies are paramount especially for plants fed by renewable sources and/or whose production is strictly connected to the users demand. The systems optimization must consider the possibility of energy storage and of the conversion among different forms of energy. In this paper, a hybrid cogeneration system composed by a cogenerative internal combustion engine, a photovoltaic plant, a boiler and a pump as turbine is investigated. Different energy storage options are included: a pack of batteries, a water reservoir and a hot thermal storage. By applying the Particle Swarm Optimization method, the devices size and hourly operation are simultaneously optimized. The minimization of the overall cost is the optimization goal, while the main constraint is the fulfilment of the user request (electricity, heat and water). Results show that the cogenerative internal combustion engine supplies the 87% of the electricity and the 19% of the heat during winter time while, during summer period, it supplies the 89% of the electricity and the all heat. The use of a pump as turbine allows to reduce the battery discharge rate and the Depth of Discharge with a consequent increase of the battery lifetime. [ABSTRACT FROM AUTHOR]

• Compression-absorption cascade system with various ionic-liquid pairs is studied. • H 2 O/ionic-liquid and NH 3 /ionic-liquid are advised for efficiency-oriented situation. • NH 3 /salt and R32/ionic-liquid are advised for operational range-oriented situation. • Explain the variation of efficiency with driving source from a new perspective. • The critical generation temperatures of various pairs are 75.53°C or higher. The compression-absorption cascade heat pump (CACHP) is an efficient way for clean heating toward carbon neutrality. Nevertheless, the working pair studied at present is only NH 3 /LiNO 3 , it is not always the most suitable fluid when used in different situations. In fact, the performance characteristics of CACHP running other working pairs have not been explored. Ionic liquid (IL) working pair is becoming an alternative to the conventional one. But current studies mainly focus on the selection within the same categories, there are few reports on performance comparison among the optimal IL working pairs which come from different categories. Hence the conclusions drawn at present are inadequate to give comprehensive recommendations about the most suitable IL working pairs. To overcome the mentioned research gaps, this work carries out a comparative analysis of the coefficient of performance (COP) for CACHP running various IL working pairs. The obtained results show that when the runnable range is the most important index, Low-GWP/IL and NH 3 /Salt are more competitive since they can be operated under more negative conditions. When the efficiency is preferred objective, H 2 O/ILs and NH 3 /ILs should be recommended, because their achievable maximum COP is 9.54% ∼ 32.78% higher than other fluids. Noteworthy, the generation temperature is not advised to exceed the critical temperature, no matter what kinds of working pairs are operated, especially when the driving source comes from primary energy consumption. As for the heating capacity of 10 kW, the critical generation temperature is 75.53°C or higher for various working pairs under different heating scenarios. [ABSTRACT FROM AUTHOR]

Abstract: Recent estimates and forecasts of the oil, gas, coal resources and their reserve/production ratio, nuclear and renewable energy potential, and energy uses are surveyed. A brief discussion of the status, sustainability (economic, environmental and social impact), and prospects of fossil, nuclear and renewable energy use, and of power generation (including hydrogen, fuel cells, micropower systems, and the futuristic concept of generating power in space for terrestrial use), is presented. Comments about energy use in general, with more detailed focus on insufficiently considered areas of transportation and buildings are brought up. Ways to resolve the problem of the availability, cost, and sustainability of energy resources alongside the rapidly rising demand are discussed. The author’s view of the promising energy R&D areas, their potential, foreseen improvements and their time scale, and last year’s trends in government funding are presented. [ABSTRACT FROM AUTHOR]

The recent rapid development of building automation systems (BAS) implies a need to identify appropriate technologies for retrofitting into homes and offices without replacing entire existing systems. Hence, the purpose of this paper is to identify research trends and patterns in BAS. To achieve this aim, a systematic literature review of BAS was conducted using topic specific keywords to categorise and summarise relevant studies. A meta-analysis followed to quantify results from collected articles. The articles were categorised into the following groups: (a) building automation architecture, (b) benefits of building automation, and (c) motivations and interactions with occupants. The results of the systematic literature review are integrated into a new research framework that highlights the importance of integrative feedback loops. The novel research framework considers qualitative and quantitative system feedback loops, which can lead to an increase in occupant acceptance as well as an uptake of BAS for industry. [ABSTRACT FROM AUTHOR]

The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale. • We define occupant-centric urban data and the pipeline to process it. • We present the different sources of urban data that are useful for data-driven modelling. • We categorise the range of applications for urban behaviour and energy modelling. • We present the recent data-driven modelling approaches for occupant behaviour and energy modelling. • We present a set of recommendations for modelling of occupant behaviour and energy in buildings at urban scale. [ABSTRACT FROM AUTHOR]

Radial (inflow) turbines with volutes have received limited attention in the body of literature for open-field wind harvesting. Few existing data in the literature indicate low maximum C p , around 0.1. This study pursues demonstration of competitive radial wind turbines (RWT) towards exploring their potentials and increase the alternatives for a future urban energy planner. RWT may have advantages in adapting the renewable energy concept into buildings due to their an order-of-magnitude lower rotational speeds compared to propeller turbines, enclosed rotors and discharge of air uniquely from the top of the turbine (therefore the building). Moreover, such features may enable low-noise and fatigue-free rotors made of less durable but recyclable materials, crucial for acoustical comfort, safety and sustainability in urban areas. Design exploration over the complex 3D flowfield of the turbine is pursued by machine learning algorithms coupled with Computational Fluid Dynamics simulations. With the achieved peak C p of 0.29 at Re c = 1.7 × 105, 103% improvement is attained relative to the existing RWTs with identical simulation methodologies. A sensitivity analysis revealed the most important parameters. The optimized turbine is compared with traditional alternatives, and it was elucidated that RWT is a competitive alternative among conventional alternatives with the described additional fundamental advantages. • C p limits of Radial Wind Turbines (RWT) are explored for the first time. • CFD simulations are coupled with Machine Learning Algorithms for optimization study. • A peak C p of 0.29 is observed at tip-speed-ratio 0.55 and Reynolds number 1.7 × 105. • C p rises to 0.36 at tip-speed-ratio 0.58 if 3D effects are omitted. • Resulting RWT model is a competitive alternative for urban wind energy harvesting. [ABSTRACT FROM AUTHOR]