Multi-criteria decision analysis (MCDA) methods have become increasingly popular in decision-making for sustainable energy because of the multi-dimensionality of the sustainability goal and the complexity of socio-economic and biophysical systems. This article reviewed the corresponding methods in different stages of multi-criteria decision-making for sustainable energy, i.e., criteria selection, criteria weighting, evaluation, and final aggregation. The criteria of energy supply systems are summarized from technical, economic, environmental and social aspects. The weighting methods of criteria are classified into three categories: subjective weighting, objective weighting and combination weighting methods. Several methods based on weighted sum, priority setting, outranking, fuzzy set methodology and their combinations are employed for energy decision-making. It is observed that the investment cost locates the first place in all evaluation criteria and CO2 emission follows closely because of more focuses on environment protection, equal criteria weights are still the most popular weighting method, analytical hierarchy process is the most popular comprehensive MCDA method, and the aggregation methods are helpful to get the rational result in sustainable energy decision-making.

Review on multi-criteria decision analysis aid in sustainable energy decision-making

Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of generation, as most renewable energy resources depend on the climate, which is why their use requires complex design, planning and control optimization methods. Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field. This paper presents a review of the current state of the art in computational optimization methods applied to renewable and sustainable energy, offering a clear vision of the latest research advances in this field.

Optimization methods applied to renewable and sustainable energy: A review

MES (multi-energy systems): An overview of concepts and evaluation models

A review on simulation-based optimization methods applied to building performance analysis

Thermal comfort and building energy consumption implications - A review

Using the fuzzy multi-criteria model to select the optimal cool storage system for air conditioning

This paper aims at the control of variable-frequency air-conditioning system that has characteristic as large inertia and pure lag. The neural network PID control in the variable-frequency air-conditioning system is introduced and simulated. In the learning algorithm of neural network PID controller, the output of system is needed to tune the weights of neural network while it is difficult to obtain. So the output of system is predicted through the algorithm of nonlinear that adopts the neural network configure. Through simulation and optimization, it is found that the neural network PID control has the capability of self-study and self-adaptation. However, the neural network PID control system sometimes has the static error. To eliminate the static error, the hybrid control of neural network PID and conventional PID is applied to the variable-frequency air-conditioning system. The hybrid control is simulated to compare the performance of changed parameters of model. The simulation finds that the hybrid control of neural network PID and PID has both the advantages of neural network and PID, such as self-studying and self-adapting and obtain faster response and better performance.

Study of Neural Network PID Control in Variable-frequency Air-conditioning System

Environmental impact analysis of BCHP system in different climate zones in China

Mathematical model of exhaust temperature control in micro gas turbine is introduced. To obtain better performance, a self-adaptive PID control is applied to the exhaust temperature control. The parameters of PID control are tuned by back propagation (BP) neural networks. In the tuning process, the plantpsilas predictive output is used to modify the weights of neural networks. The plantpsilas output is also predicted by BP neural networks and it is nonlinear prediction which improves the predictive accuracy. The effectiveness and efficiency of the proposed control strategy is demonstrated by applying it to the exhaust temperature control. The simulations show that the dynamic responses of the exhaust control system can be effectively improved and the anti-disturbance of the proposed controller is better than that of the PID controller.

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Adaptive PID control with BP neural network self-tuning in exhaust temperature of micro gas turbine

The controlled objects are modeled in heating, ventilating and air-conditioning (HVAC) system, mainly including the heat exchanger and the air-conditioning space. The HVAC system has large inertia, pure lag and nonlinear characteristic. The uncertain disturbance factors affect the control performance. To obtain better performance in HVAC system, this study proposes a hybrid Cerebellar model articulation controller (CMAC)-PID control system, which combines the CMAC neural network and general PID control. This method realized the feedback control by using traditional PID controller to enhance the stability and reject the disturbance and realized the feed forward control by using CMAC neural network to increase the response speed and control precision in HVAC system. The simulation results show the hybrid CMA-PID control system possesses the advantages of high precision, realtime standard, strong ability of anti-interfere and robustness.

Chunfa Zhang

Papers

Using the fuzzy multi-criteria model to select the optimal cool storage system for air conditioning

Study of Neural Network PID Control in Variable-frequency Air-conditioning System

Environmental impact analysis of BCHP system in different climate zones in China

Adaptive PID control with BP neural network self-tuning in exhaust temperature of micro gas turbine

Hybrid CMAC-PID Controller in Heating Ventilating and Air-Conditioning System