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To identify the influence of digital transformation on energy consumption, this paper introduces digitalization and energy elements into the multi-sector energy efficiency analysis model. Furthermore, this paper empirically tests the influence of digital transformation on energy efficiency from the micro level. The results show that the digital transformation indeed improves the enterprises energy efficiency, in which enterprises with higher energy efficiency are more affected. The improvement effect of digital transformation on enterprise energy efficiency continues to increase over time. In comparison, digital transformation shows more effective improvement effect on energy efficiency in enterprises that substantive transformation priority, with same group and maturity stage. Mechanism analysis shows that digital transformation can improve the energy efficiency of enterprises by reducing costs, increasing income, improving production efficiency and promoting technological innovation. In addition, the digital inclusive finance, government subsidies, intellectual property protection and service industry opening show positive regulatory effects. This paper proposes that, with the goal of peak carbon dioxide emissions and carbon neutrality, enterprises should actively increase digital investment and strengthen digital management of energy utilization. [Display omitted] • Digital transformation indeed improves the enterprises energy efficiency. • The promotion effect of digital transformation increases over time. • Digital transformation drives efficiency improvement and technological innovation. • Digital transformation contributes to cost reduction and benefit increase. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative decision support system for improving ship energy efficiency. It combines an engine optimization model and an artificial neural network (ANN). Real-time data regarding the ship and engine performance is obtained along a specific ship navigation, and an engine optimization model is built using Ricardo Wave software. This model is validated with high accuracy. Ship-specific parameters are derived, and fuel consumption is estimated using ANN models with different structures. The study identifies the most suitable ANN model with 1 hidden layer and 5 neurons based on error metric evaluation where the 0.99697 R2, 0.00035 RMSE, and 0.06470 MAPE scores are calculated. This approach offers a cost-effective solution for shipping companies to monitor critical engine parameters in real-time without investing in sensors or data collection systems. Thus, it contributes to dealing with the problem of the scarcity of analyzable data in the maritime literature, which is one of the significant issues in the papers related to energy efficiency, machine learning, and deep learning. It is an innovative approach since the parameters that are related to real-time operations rather than considering only instruction book information are aggregated, and the produced data is analyzed by an intelligent tool. • A system is proposed to control ship energy efficiency level by combining an optimization model and a neural network • A simulation model is created in the Ricardo Wave tool and the engine model is validated by actual data. • The ship fuel consumption is estimated by an artificial neural network based on the derived input parameters. • The prediction process is evaluated by error metrics to determine its success. [ABSTRACT FROM AUTHOR]

Energy efficiency is crucial for the sustainable operation of industrial and urban sectors. However, practicing engineers seldom have access to open-source tools that can readily evaluate and compare scenarios. In this work, a novel web-based tool called ROSMOSE is developed and proposed for analyzing the energy efficiency of industrial and urban systems and comparing potential process integration options. This optimization framework computes and graphically represents the minimum energy requirements, the pinch temperatures, and the Grand and integrated Carnot composite curves of process systems. Results are used to design utility systems that meet energy demands, while minimizing a specified objective function, such as total cost or environmental impacts. The Quarto environment, in which ROSMOSE is built upon, allows the integration of various open-source software and tools, such as database handling software, process modeling suites, and optimization solvers, along with interactive data visualization and reporting tools. This paper discusses the application of ROSMOSE for the energy integration and total site optimization of a dairy process consisting of milk treatment, and cream and cheese production to demonstrate the tool features. Other integration options, such as the use of heat pumps or solar panels, and the production of soft drinks or biogas as value-added byproducts, are also proposed and evaluated. As a result, from 75 % to 90 % in fuel savings and up to 80 % CO 2 emissions reduction in the dairy plant are identified by optimizing the utility systems integration. Moreover, complete electrification via heat pumps eliminates any fossil fuel needs and it results in 85 % energy savings and up to 96 % CO 2 emissions reduction. The activation of renewable electricity sources, such as solar power, made plausible this fully electrified scenario. Finally, optimized waste management strategies led to in-house fuel production and net export of biogas to the grid, but they also reduce cheese yield by prioritizing the biogas production from whey upgrading process. In this way, decision-makers have access to a clear performance comparison for a list of system configurations to draw informed trade-offs. A similar approach could be adopted to aid decision-making for the design and operation of other industrial and urban energy systems using ROSMOSE. [Display omitted] • ROSMOSE is an open-source energy integration, optimization and reporting tool. • Pinch analysis and other assessments of decarbonization strategies can be performed. • The process integration and waste valorization of a dairy plant is used as case study. • Above 75 % energy saving is achieved by process integration and optimal utility selection. • 96 % reduction in CO 2 emissions and on-site biogenic energy sources are identified. [ABSTRACT FROM AUTHOR]

Energy performance indices are widely used methods for determining and assessing the efficiency of buildings in operation. However, methodologies for indices that compare heat consumption among buildings and at higher temporal granularity than yearly are needed. This makes it possible for consumers to compare themselves to others to encourage them to improve their energy efficiency. For a district heating system, it also provides the district heating company with an overview of consumers' energy efficiency (and consumers' efficiency must be comparable). This is critical due to the interconnectedness of district heating systems, as consumers can indirectly have negative impacts on each other's operations. Having an overview of the efficiency of consumers can provide useful information for the district heating companies to detect unusual consumption and possible faults. This paper therefore has created a framework for the comparison of heat consumption among buildings, by taking into account various contextualities to make comparisons possible. The framework has been tested on a case study, and its capabilities are showcased. • Leveraging building metadata to allow energy consumption comparison among buildings. • Creation of heating consumption performance indexes for use by end-consumers. • Enabling of higher temporal resolution performance indexes increasing usability. • Fault detection based on performance indexes. • Showcasing of methodology on real consumption data from a suburb in Denmark. [ABSTRACT FROM AUTHOR]

The average electricity consumption per capita in China still has significant room for growth. Under the current rigid residential electricity pricing, it is very important to study how factors other than price can promote electricity conservation among residents. This paper uses micro-survey data of 2018 and 2020 to analyze energy efficiency and the rebound effect through a stochastic energy demand frontier model, as well as to examine the role of news propaganda in this context. The research conclusions are as follows: (1) News propaganda promoting electricity saving can weaken the rebound effect of electricity use, which can lead to better electricity savings. (2) The energy efficiency of residential electricity has improved significantly compared to previous years, and the rebound effect has decreased. (3) Electricity prices have a negative impact on electricity consumption, while income, household size, home area, and extreme temperatures have a positive impact. Based on these conclusions, we propose policy recommendations to address the current inflexibility in residential electricity pricing and to improve the efficiency of electricity use among residents. • News propaganda promoting electricity saving can weaken the rebound effect. • The energy efficiency of residential electricity has improved. • The rebound effect of residential electricity has decreased. • News propaganda can be a complement to the inflexibility of pricing policy. [ABSTRACT FROM AUTHOR]

Using the unique micro-data of industrial enterprises' Nitrogen oxides (NOx) and Sulfur oxides (SOx) emissions covering 58,725 enterprise-year observations, this paper investigates how digitalization affect energy efficiency. The empirical results show that regional digitalization can significantly promote the improvement of energy efficiency of industrial enterprises. Specifically, the result of PSM-DID method indicates that the impact of digitalization on energy efficiency is significantly enhanced by the "National innovative pilot cities" policy, while the dynamic result validates the continuity of industrial enterprises' energy efficiency. Moreover, regional digitalization has superior impact on enterprises within eastern region enterprises, the manufacturing sector, and with elevated liquidity and profitability levels. Finally, moderating effect suggests that technological innovation expand the positive impact of digitalization on enterprises' energy efficiency. This study confirms regional digitalization can promote the energy efficiency of Chinese industrial enterprises, providing the policy suggestions to the energy transition in China. • Measure the regional digitalization level of Prefecture-level city and the energy efficiency at Industrial enterprise-level. • Findings demonstrated that digitalization increases energy efficiency. • The impact of digitalization on energy efficiency can be enhanced by the "National innovative pilot cities" policy. • Technological innovation expands the positive impact of digitalization on enterprises' energy efficiency. [ABSTRACT FROM AUTHOR]

The regenerator plays a crucial role in determining the energy efficiency of green liquid desiccant air-conditioning systems. This paper presents a novel air gap membrane distillation regenerator featuring heat recovery and portable water production. Based on a validated numerical model, performance evaluation of the regenerator combined with an external heat exchanger is conducted in terms of regeneration capacity (RC) and energy consumption (Q in). The comparison of flow patterns demonstrates that the counter-flow has a slightly higher RC , while the parallel-flow has a significantly lower Q in and is therefore preferable from the perspective of COP. The effects of key parameters including feed and coolant desiccant inlet temperatures (T f,in & T c,in), mass flow rate (m dil), membrane length (L), air gap thickness (δ ag), desiccant concentration (X dil) and solution heat exchanger effectiveness (ε she) on the performance are investigated. The results show that T f,in is the dominant factor on performance compared to T c,in and m dil , and increasing L leads to a marked growth in both RC and COP regardless of the raised Q in. Additionally, it is found that narrowing the air gap from 3 mm to 1 mm can improve RC and COP by 69.9 % and 50.1 % respectively. As for ε she , it has no influence on RC but remarkable impacts on Q in and COP. Moreover, the heat recovery by the coolant channel is proved to save nearly 8 % of energy input under the involved conditions. Finally, the NSGA-Ⅱ based multi-objective optimization is performed to obtain the Pareto front for maximizing RC and minimizing Q in. And the optimal solution through TOPSIS decision-making offers a RC = 975.8 g/h and a Q in = 1938 W. The findings provide a basis for design and application of the novel regenerator. • A novel air gap membrane distillation liquid desiccant regenerator is investigated. • Parallel-flow pattern is superior to counter-flow pattern in terms of energy efficiency. • Heat recovery by the regenerator can achieve about 8 % energy savings. • Effects of various design and operation parameters on the performance are explored. • Trade-off between regeneration capacity and energy consumption is realized by NSGA-Ⅱ. [ABSTRACT FROM AUTHOR]

With the facilitation of agricultural modernization, China's agricultural energy consumption continues to rise. This paper empirically tests the agricultural energy rebound effect and its influencing factors in China during 1999–2020. It is believed that the improvement of energy efficiency has not led to the expected energy-saving effect in agriculture due to the "Jevons paradox" of energy efficiency and energy consumption. The average energy efficiency of China's agricultural energy consumption was 0.446, and the average energy rebound effect was 68.9 %. The level of agricultural mechanization, the effective irrigated area of farmland and agricultural investment have a positive impact on the rebound effect of agricultural energy, and the direct effects are 0.062, 0.008 and 0.064, respectively, while agricultural labor force and energy price have a negative impact on the rebound effect of agricultural energy. In particular, technology spillover and scale spillover led by agricultural technology progress have strong negative space spillover effect. As it is argued, 1 % of agricultural technology progress can reduce 3.5 % of agricultural energy rebound effect in surrounding areas. In order to reduce the energy rebound effect, it is suggested to increase the subsidies to agricultural labors and rationally formulate regional differentiated energy regulation policies. • The internal mechanism of rebound effect is expounded from the view of producers and consumers. • The average energy efficiency of China's agricultural energy consumption was 68.9 % during 1999–2020. • The determinants of the agricultural energy rebound effect were further discussed by considering the spatial spillover effect. [ABSTRACT FROM AUTHOR]

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies. • A normalized energy consumption model is presented, combining two energy usage. • A novel eco-driving strategy is developed based on the proposed energy consumption. • The algorithm suits both free traffic and car-following scenarios with safety. • The characteristics of the four eco-driving strategies are compared. • The proposed strategy is verified by both simulation and vehicle-in-the-loop tests. [ABSTRACT FROM AUTHOR]