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With the diversification of users' energy demands, accurate load forecasting is an important prerequisite for optimal scheduling and economic operation of the system, but a single‐load forecasting method cannot effectively predict multi‐energy loads accurately. Therefore, this paper proposes a multi‐energy load forecasting method based on bidirectional long short‐term memory (BiLSTM) and parallel feature extraction networks. Firstly, residual network and convolutional block attention module were used to extract the spatial coupling features of multi‐energy load data. Secondly, BiLSTM is used to capture the temporal features and long‐term dependencies in the load data, and the spatial coupling features are fused to obtain non‐linear prediction results. Finally, the non‐linear prediction results and the linear prediction results obtained by using multi‐energy linear regression were linearly superimposed to obtain the final prediction results. In this paper, IES load data of Tempe Campus of Arizona State University was used to verify and compared with several existing methods, and the results showed that Weighted Mean Absolute Percentage Error decreased by more than 20%. [ABSTRACT FROM AUTHOR]

To attain a thorough examination of the dynamic interaction mechanism between China's utilization of water resources and its economic and social development, this paper innovatively introduces a Western theoretical framework within an Eastern context. The aim is to quantify the process of decoupling and synergistic evolution from 2006 to 2020. Decoupling signifies achieving economic growth while reducing resource use or mitigating environmental impacts, whereas synergy denotes enhancing economic efficiency while contributing to environmental protection. This paper employs the Tapio elastic coefficient method to construct a decoupling model. A collaborative model, based on the entropy weight Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and weighted average methods, also assesses the organization and coordination of subsystems within the complex water‐economic‐social system. The findings reveal a strong decoupling between China's water resource management and economic and social systems, as indicated by a consistently negative decoupling index. Similarly, the economic system exhibits a degree of inefficiency influenced by factors like economic cyclicality and resource allocation. In contrast, the social system experienced a decline, particularly during the pandemic (2019–2020), leading to a social instability. This study provides valuable policy formulation and sustainable development insights, contributing to a comprehensive understanding of the intricate dynamics between water utilization, economy, and society. [ABSTRACT FROM AUTHOR]

There is a widespread agreement that the current linear economic system is unsustainable due to the physical boundaries of our planet and must be replaced by a circular one, that is, by a circular economy (CE). Previous studies have theoretically proposed that eco‐innovations (EIs), or innovations that reduce the environmental impacts of consumption and production processes, are a key element to improve the circular use of resources. However, the analysis of the drivers of such a transition to a CE through EI, particularly at the micro‐level of firms, has not received attention in the past. The aim of this paper is to cover this gap in the literature. It quantitatively analyses the role of influencing factors (drivers and barriers) in that relationship, with a focus on the company's organisational ('soft') factors, rooted in organisational culture and strategy. With the help of data collected through a survey of Spanish small and medium‐sized firms in the industrial sector, a regression‐based moderation analysis is carried out. Therefore, four moderation models were defined based on organisational factors derived from the previous literature (sustainability, future, stakeholder and learning/knowledge orientation in firms. The results suggest that, indeed, two of those factors (a high level of sustainability and knowledge orientation) are particularly relevant drivers in the transition to the CE through EI. Thus, if the aim of policy makers and corporate decision makers is to increase the circularity of the economy or their firms, policies and measures should be targeted at these two factors. [ABSTRACT FROM AUTHOR]

The economic dispatch (ED) problem is very important in the economics of power systems, and a suitable dispatch algorithm can help power plants save huge amounts of money and use energy efficiently. To ensure the economics of the power system dispatch strategy, this paper proposes a quantum‐behaved particle swarm improvement algorithm for solving the ED problem. First, the algorithm addresses the problem that the classical quantum‐behaved particle swarm optimization (QPSO) tends to be premature and falls into local optimality. We propose a method to calculate the mean best position by assigning weights to different particles, by adopting the idea of concentration selection in the immune concentration regulation mechanism, and designing a Weight‐Concentration selection probability function, so that those particles located outside the local optimal solution region but with smaller fitness value and better evolutionary trend can be retained. Second, to make the algorithm focus on global search at the beginning of the iteration and more focused search at the end to enhance the neighborhood search capability at the later stage, a decreasing function of the slope K in the Weight‐Concentration selection probability function is designed so that the influence of the weighting term on the calculation of the mean best position decreases gradually. The algorithm is evaluated with four power systems, and the results show that the overall performance of the improved algorithm is better than other algorithms in solving the ED problem in terms of minimum solution, convergence, and stability. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Social scientists appeal to various "structures" in their explanations including public policies, economic systems, and social hierarchies. Significant debate surrounds the explanatory relevance of these factors for various outcomes such as health, behavioral, and economic patterns. This paper provides a causal account of social structural explanation that is motivated by Haslanger (2016). This account suggests that social structure can be explanatory in virtue of operating as a causal constraint, which is a causal factor with unique characteristics. A novel causal framework is provided for understanding these explanations–this framework addresses puzzles regarding the mysterious causal influence of social structure, how to understand its relation to individual choice, and what makes it the main explanatory (and causally responsible) factor for various outcomes. [ABSTRACT FROM AUTHOR]

Direct current optimal power flow (DC‐OPF) problems need to be solved more frequently to maintain safety and economic power system operation. Traditional solvers take too much time to get optimal results. To overcome it, a new self‐supervised augmented Lagrangian neural network (ALNN) is proposed to solve DC‐OPF problem. The proposed ALNN consists of two neural networks: the control net and the penalty net. The control net predicts active power of generators; the penalty net updates the Lagrangian multipliers. The equality constraints are embedded into the control net to guarantee no equality violations. The generalized reduced gradient method is used to reduce theviolations of inequality constraint. The effectiveness of the proposed model is demonstrated on IEEE 118‐bus. The results show that with the help of equality embedding, the equality constraints are always satisfied, which in turn improves the feasibility of ALNN. Compared to the state‐of‐art models, the proposed model has higher feasibility and less constraint violations without comprising optimality. What is more, most of the inactive constraints can be found during the training process and then they are used to speed up the post‐processing part. [ABSTRACT FROM AUTHOR]