The supermatrix technique of the analytic hierarchy process can effectively address complex energy decision problems, with potential for generalization to multiple-level networks.

The analytic network process in energy policy planning

The Poisson disk process (PDP) model accurately models the distribution of energized nodes in wirelessly powered networks with positive energy correlation, maintaining analytical tractability.

A Tractable Model for Wirelessly Powered Networks With Energy Correlation

Analytical network process (ANP) is a multi-criteria decision-making method used in various application areas, including health, safety, and environmental management.

Analytic network process: An overview of applications

The graph reduction method effectively analyzes complex energy-integrated process networks, providing insights on effective control strategies for reactor-heat exchanger and distillation column networks.

Graph reduction of complex energy‐integrated networks: Process systems applications

The generalized analytic network process (G-ANP) allows multiple forms of preferences, enabling multi-criteria decision making in complex scenarios.

Generalized analytic network process

Data analysis plays a crucial role in intelligent energy networks, aiding in clustering, demand forecasting, optimization, pricing, monitoring, and diagnostics.

The Role of Data Analysis in the Development of Intelligent Energy Networks

Nanoscale energy conversion processes can be studied using a network representation, allowing for cycle analysis and calculation of zero-current limits.

Network analysis of nanoscale energy conversion processes

Artificial neural networks (ANNs) can effectively predict energy consumption in buildings more reliably than traditional simulation models and regression techniques, offering a promising design tool in various areas of building services engineering.

Energy analysis of a building using artificial neural network: A review

This paper presents a graph-theoretic framework for analyzing and controlling complex energy-integrated process networks, identifying weak and strong connections between process units.

Reduction of complex energy-integrated process networks using graph theory

The new graphical method for Pinch Analysis in heat recovery systems can identify potential modifications for improved energy performance and reduce fuel and cooling water requirements.

A new graphical method for Pinch Analysis applications: Heat exchanger network retrofit and energy integration

Artificial neural networks trained with measurement data achieve significantly higher prediction accuracy in Directed Energy Deposition quality assurance, especially for complex geometries.

Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals

Analytical energy functions can be constructed for network-preserving power system models, covering existing stability models and a new model with detailed generator and hybrid load representations.

Constructing Analytical Energy Functions for Network-Preserving Power System Models

The integrated method for analyzing electricity and heat networks requires less iteration than the decomposed method, highlighting the importance of considering both energy vectors in integrated systems.

Combined analysis of electricity and heat networks

This paper demonstrates that optimizing energy consumption in multihop wireless embedded and sensor networks is crucial for maintaining network connectivity and reducing overall energy consumption.

Energy Optimization in Multihop Wireless Embedded
and Sensor Networks

Free-energy transduction in chemical reaction networks occurs when balanced processes are driven against their spontaneous direction, resulting in thermodynamic machines and central energy metabolism.

Free-energy transduction in chemical reaction networks: From enzymes to metabolism.

Energy harvesting cognitive radio networks can improve energy efficiency and spectral efficiency by opportunistically accessing licensed spectrum, with optimal detection thresholds maximizing throughput under energy causality and collision constraints.

Achievable Throughput of Energy Harvesting Cognitive Radio Networks

This paper proposes a novel method for wireless networks to coexist, where low-power mobiles harvest energy from nearby primary transmitters, maximizing secondary network throughput under outage-probability constraints.

Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks

Energy-aware design methodologies can significantly enhance the lifetime of wireless sensor networks by optimizing power consumption across all aspects of the network.

Energy-aware wireless microsensor networks

Computer-based technologies, such as fuzzy logic and artificial neural networks, can optimize energy use in wastewater purification processes.

Energy Optimization of Wastewater Cleaning Technologies

This paper presents transaction-level modeling (TLM) for polyhedral process networks, enabling efficient simulation and energy consumption prediction in streaming embedded systems using SystemC and Daedalus.

Transaction Level Energy Modeling of Polyhedral Process Networks

Is Analytical network process used in energy?

These studies suggest that the Analytical Network Process (ANP) is used in various application areas, but there is no specific mention of its use in energy.