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An improved mayfly algorithm is proposed for the energy saving optimization of parallel chilled water pumps in central air conditioning system, with the minimum energy consumption of parallel pump units as the optimization objective and the speed ratio of each pump as the optimization variable for the solution. For the problem of uneven random initialization of mayflies, the variable definition method of Circle chaotic mapping is used to make the initial position of the population uniformly distributed in the solution space, and the mayfly fitness value and the optimal fitness value are incorporated into the calculation of the weight coefficient, which better balances the global exploration and local exploitation of the algorithm. For the problem that the algorithm is easy to fall into the local optimum at the later stage, a multi-subpopulation cooperative strategy is proposed to improve the global search ability of the algorithm. Finally, the performance of the improved mayfly algorithm is tested with two parallel pumping system cases, and the stability and time complexity of the algorithm are verified. The experiments show that the algorithm can get a better operation strategy in solving the parallel water pump energy saving optimization problem, and can achieve energy saving effect of 0.72% 8.68% compared with other optimization algorithms, and the convergence speed and stability of the algorithm have been significantly improved, which can be better applied to practical needs. [ABSTRACT FROM AUTHOR]

Short-term energy consumption prediction of buildings is crucial for developing model-based predictive control, fault detection, and diagnosis methods. This study takes a university library in Xi'an as the research object. First, a time-by-time energy consumption prediction model is established under the supervised learning approach, which uses a long short-term memory (LSTM) network and a Multi-Input Multi-Output (MIMO) strategy. The experimental results validate the model's validity, which is close enough to physical reality for engineering purposes. Second, the potential of the people flows factor in energy consumption prediction models is explored. The results show that people flow has great potential in predicting building energy consumption and can effectively improve the prediction model performance. Third, a diagnostic method, which can recognize abnormal energy consumption data is used to diagnose the unreasonable use of the building during each hour of operation. The method is based on differences between actual and predicted energy consumption data derived from a short-term energy consumption prediction model. Based on actual building operation data, this work is enlightening and can serve as a reference for building energy efficiency management and operation. [ABSTRACT FROM AUTHOR]

Reasonable distribution of cooling load between chiller and ice tank is the key to realize the economical and energy-saving operation of ice-storage air-conditioning (ISAC) system. A multi-objective optimization model based on improved firefly algorithm (IFA) was established in this study to fully exploit the energy-saving potential and economic benefit of the ISAC system. The proposed model took the partial load rate of each chiller and the cooling ratio of the ice tank as optimization variables, and the lowest energy consumption loss rate and the lowest operating cost of the ISAC system were calculated. Chaotic logic self-mapping was used to initialize population to avoid falling into local optimum, and Cauchy mutation was used to increase the population's diversity to improve the algorithm's global search ability. The experimental results show that compared with the operation strategy based on constant proportion, particle swarm optimization (PSO) algorithm, and firefly algorithm (FA), the optimal operation strategy based on IFA can achieve more significant energy-saving and economic benefits. Meanwhile, the convergence accuracy and stability of the algorithm are significantly improved. Practical application: The optimized operation strategy of the ice-storage air-conditioning system can reduce energy loss and operating costs. The traditional operation strategies have the problems of low optimization precision and poor optimization effect. Therefore, this study presents an optimal operation strategy based on IFA. The convergence accuracy and stability of the algorithm are increased after the algorithm is improved. The operation strategy can get the maximum energy-saving effect and economic benefit of the ISAC system. [ABSTRACT FROM AUTHOR]

Energy consumption prediction can provide reliable data support for energy scheduling and optimization of office buildings. It is difficult for traditional prediction model to achieve stable accuracy and robustness when energy consumption mode is complex and data sources are diverse. Based on such situation, this paper raised an approach containing the method of comprehensive similar day and ensemble learning. Firstly, the historical data was analyzed and calculated to obtain the similarity degree of meteorological features, time factor and precursor. Next, the entropy weight method was used to calculate comprehensive similar day and applied to the model training. Then the improved sine cosine optimization algorithm (SCA) was applied to the optimization and parameter selection of a single model. Finally, an approach of model selection and integration based on dominance was proposed, which was compared with Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), with a large office building in Xi 'an taken as an example to analysis showing that compared with the prediction accuracy, root mean square percentage error (RMSPE) in the ensemble learning model after using comprehensive similar day was reduced by about 0.15 compared with the BP model, and was reduced by about 0.05, 0.06 compared with the SVR and LSTM model. Respectively, the mean absolute percentage error (MAPE) was reduced by 12.02%, 6.51% and 5.28%. Compared with several other integration methods, integration model based on dominance reduced absolute error at all times. Accordingly, the proposed approach can effectively solve problems of low accuracy and poor robustness in traditional model and predict the building energy consumption efficaciously. [ABSTRACT FROM AUTHOR]

Paying attention to the feature engineering problems is the basis for constructing a more accurate building energy consumption prediction model, which helps debug, control, and operate building energy management systems. Therefore, in this paper, an integrated energy consumption prediction model considering spatial characteristics in time series data is proposed to predict the short-term energy consumption of educational buildings, and the influence of features on the model is analyzed using the cooperative game theory SHAP method, and the optimal number of features is determined by ablation analysis. The proposed model is validated by an educational building in Xi'an, Shaanxi Province. The results show that compared with other energy consumption prediction models, the RMSE value of the integrated energy consumption prediction model is reduced by 13.64%–34.55%, and the MAE value is reduced by 10.25%–30.54%, which has higher prediction accuracy. In addition, this paper also investigates the minimum amount of data and the number of features required for the training of the building energy prediction model, and the integrated energy prediction model can still effectively predict building energy consumption when the training samples are minimal and the number of features is appropriate. • Feature engineering method using deep learning to mine features. • Analyzed the impact of features on predictive models using cooperative game theory. • Analyzed the impact of dataset size and number of features on model predictions. • Developed an integrated model for building energy consumption prediction. • The main factors affecting energy consumption in educational buildings are discussed. [ABSTRACT FROM AUTHOR]

Energy consumption prediction is of great practical significance for the study of energy-saving scheduling of building air-conditioning systems. However, there are two problems in prediction models: (1) A single prediction model is difficult to solve the nonlinear problem of building energy consumption data. (2) A single prediction result can hardly reflect the uncertainty in the building operation process. Therefore, in this study, a hybrid prediction model with an improved whale optimization algorithm- optimization support vector machine (IWOA-SVM) is proposed based on the air conditioning system of a shopping mall in Xi'an. The WOA is improved using the opposition-based learning strategy (OBL), differential evolution algorithm (DE), and introducing a control parameter λ. The Confidence Interval (CI) of the model fitting error is used to represent the prediction results. The results show that the root mean square error (RMSE) of the optimized model is reduced by 9.5, and the (RMSE) of the total energy consumption prediction value after adding CI is >1.68. The method achieves higher model accuracy, reflecting the operational fluctuations of the building. [ABSTRACT FROM AUTHOR]

As the air-conditioning water system is designed according to the maximum load, the system will deviate from its optimum state while operating under partial load. Therefore, it is critical that the numerous operating parameters of the various equipments in the system are dynamically adjusted in an effective and timely manner to maximize the operational energy efficiency of the system. To this end, an improved parallel artificial immune system (IPAIS) algorithm is proposed to determine the optimal operating parameters of the system under different loads. Before optimization, the power consumption model is developed using generalized regression neural network (GRNN) combined with mechanism model for each kind of equipment in the system. Afterwards, the optimal control problem is described with the objective of minimizing the total power consumption of all equipments and considering the relevant constraints. Subsequently, the IPAIS is developed to solve the problem by introducing four improvement strategies. Finally, a simulation experiment is conducted using an actual case of an air-conditioning water system. The results show that the developed power consumption model performs well in accuracy, robustness and generalization ability, and the total system energy consumption is reduced by 15.19% after optimization. Meanwhile, the IPAIS is extended to five variants to confirm the functionality and effectiveness of each improved strategy. Furthermore, the optimization performance of IPAIS in the actual system is comprehensively verified and analyzed using an experimental platform. Compared with the comparison algorithms, IPAIS is able to achieve superior optimization results and presents significant advantages in convergence, robustness and computational complexity. [ABSTRACT FROM AUTHOR]

Aiming at the problem of the dynamic hydraulic balance regulation of chilled water pipe network in central air-conditioning system, a distributed iterative optimization algorithm is proposed based on the novel distributed control architecture and the alternating direction method of multipliers (ADMM) with regular term. Firstly, the total flow and the minimum water pressure difference between supply and return are calculated according to the flow demand of each air handling unit. Furthermore, based on the ADMM with regular term, the optimal operation of parallel pumps is accomplished. Finally, the dynamic hydraulic balance of chilled water pipe network is achieved with the minimum energy consumption, and the on-demand flow distribution is realized. After that, a central air-conditioning system is taken as an example to verify the performance of the proposed algorithm. Results show that the proposed algorithm is a distributed optimization algorithm with high precision, high stability and high energy saving. Compared with one strategy that is not optimized, the operation strategy optimized by the proposed algorithm can save energy about 28.54% while realizing the dynamic hydraulic balance of the pipe network. • The generality of the algorithm in the distributed control architecture is improved. • Hydraulic balance optimization problem of chilled pipe network is decoupled. • ADMM with regular term solves distributed optimal operation problem of parallel pumps. • ADMM saves energy 28.54%, realizing the dynamic hydraulic balance of the pipe network. [ABSTRACT FROM AUTHOR]