Prediction of Building Energy Consumption Based on IPSO-CLSTM Neural Network

Accurate prediction of building load is essential for energy saving and environmental protection. Exploring the impact of building characteristics on heating and cooling load can improve energy efficiency from the design stage of the building. In this paper, a prediction model of building heating and cooling loads is proposed, which based on Improved Particle Swarm Optimization (IPSO) algorithm and Convolution Long Short-Term Memory (CLSTM) neural network model. Firstly, the characteristic variables are extracted and evaluated by Spearman’s correlation coefficient method; Then the prediction model based on the CLSTM neural network is constructed to predict building heating and cooling load. The IPSO algorithm is adopted to solve the problem that manual work cannot precisely adjust parameters. In this method, the optimization ability of the PSO algorithm is improved by changing the updating rule of inertia weight and learning factors. Finally, the parameters of the neural network are taken as IPSO optimization object to improve the prediction accuracy. In the experimental stage of this paper, a variety of algorithm models are compared, and the results show that IPSO-CLSTM can get the best results in the prediction of heating and cooling load.

Prediction of air particulate matter in Beijing, China, based on the improved particle swarm optimization algorithm and long short-term memory neural network

Intelligent algorithms using deep learning can help learn feature data with nonlinearity and uncertainty, such as time-series particle concentration data. This paper proposes an improved particle swarm optimization (IPSO) algorithm using nonlinear decreasing weights to optimize the hyperparameters, such as the number of hidden layer neurons, learning rate, and maximum number of iterations of the long short-term memory (LSTM) neural network, to predict the time series for air particulate concentration and capture its data dependence. The IPSO algorithm uses nonlinear decreasing weights to make the inertia weights nonlinearly decreasing during the iteration process to improve the convergence speed and capability of finding the global optimization of the PSO. This study addresses the limitations of the traditional method and exhibits accurate predictions. The results of the improved algorithm reveal that the root means square, mean absolute percentage error, and mean absolute error of the IPSO-LSTM model predicted changes in six particle concentrations, which decreased by 1.59% to 5.35%, 0.25% to 3.82%, 7.82% to 13.65%, 0.7% to 3.62%, 0.01% to 3.55%, and 1.06% to 17.21%, respectively, compared with the LSTM and PSO-LSTM models. The IPSO-LSTM prediction model has higher accuracy than the other models, and its accurate prediction model is suitable for regional air quality management and effective control of the adverse effects of air pollution.

The Effects of Weightage Values with Two Objective Functions in iPSO for Electro-Hydraulic Actuator System

In this paper, the Proportional-Integral-Derivative (PID) controller with improved Particle Swarm Optimization (iPSO) algorithm is proposed for the positioning control of nonlinear Electro-Hydraulic Actuator (EHA) system. PID controller is chosen to control the EHA system due to its popularity in industrial applications. The PID controller parameters will be tuned by using the iPSO algorithm to get the lowest overshoot percentage and steady-state error. The conventional PSO algorithm has only one objective function to get the optimum parameters. However, this is not enough to increase the control performance of the EHA system. Therefore, an improved Particle Swarm Optimization (iPSO) that includes the mean error and overshoot percentage as the two objective functions is proposed in this paper. The most popular method in PSO that included two objective functions is Linear Weight Summation (LWS). In this method, the two objective functions are combined with certain weightage into one equation to give the best control performance. This paper focuses on determining the suitable weightage between these two objective functions so that the EHA system can produce the best control performance with less overshoot and less error. Overshoot percentage and steady-state error are used to indicate the best control performance. The results showed that EHA system can perform better by using suitable weightage between the mean error and overshoot percentage.

An Intelligent Algorithm for Solving the Efficient Nash Equilibrium of a Single-Leader Multi-Follower Game

This aim of this paper is to provide the immune particle swarm optimization (IPSO) algorithm for solving the single-leader–multi-follower game (SLMFG). Through cooperating with the particle swarm optimization (PSO) algorithm and an immune memory mechanism, the IPSO algorithm is designed. Furthermore, we define the efficient Nash equilibrium from the perspective of mathematical economics, which maximizes social welfare and further refines the number of Nash equilibria. In the end, numerical experiments show that the IPSO algorithm has fast convergence speed and high effectiveness.

An Intelligent Fault Diagnosis Method for Transformer Based on IPSO-gcForest

Transformers are the main equipment for power system operation. Undiagnosed faults in the internal components of the transformer will increase the downtime during operation and cause significant economic losses. Efficient and accurate transformer fault diagnosis is an important part of power grid research, which plays a key role in the safe and stable operation of the power system. Existing traditional transformer fault diagnosis methods have the problems of low accuracy, difficulty in effectively processing fault characteristic information, and superparameters that adversely affect transformer fault diagnosis. In this paper, we propose a transformer fault diagnosis method based on improved particle swarm optimization (IPSO) and multigrained cascade forest (gcForest). Considering the correlation between the characteristic gas dissolved in oil and the type of fault, firstly, the noncode ratios of the characteristic gas dissolved in the oil are determined as the characteristic parameter of the model. Then, the IPSO algorithm is used to iteratively optimize the parameters of the gcForest model and obtain the optimal parameters with the highest diagnostic accuracy. Finally, the diagnosis effect of IPSO-gcForest model under different characteristic parameters and size samples is analyzed by identification experiments and compared with that of various methods. The results show that the diagnostic effect of the model with noncode ratios as the characteristic parameter is better than DGA data, IEC ratios, and Rogers ratios. And the IPSO-gcForest model can effectively improve the accuracy of transformer fault diagnosis, thus verifying the feasibility and effectiveness of the method.

Study on the planning of rural land spatial utilization by improved particle swarm optimization

The planning of rural land space utilization is a very important problem. In this paper, the objective function of rural land use planning was analyzed firstly, and then the improved particle swarm optimization (IPSO) algorithm was obtained by improving the inertia weight for solution. The results showed that the land space use in the study area was more reasonable after the planning based on the IPSO algorithm, the forest land and construction land increased, the area of grassland, cultivated land and water area reduced appropriately, the aggregation degree of all types of land improved, and the space distribution was more planned, which was more conducive to production activities. The analysis results verify the effectiveness of the IPSO method in land space use planning, which can improve the efficiency and benefit of land space use, and it can be popularized in practical application.

Gravity field inversion using Improved Particle Swarm Optimization (IPSO) for estimation of sedimentary basin basement depth

In this paper, for the first time an Improved Particle Swarm Optimization (IPSO) algorithm, is developed to evaluate the 2.5-D basement of sedimentary basin and consequently to simulate its bottom, by using the density contrast that varies parabolically with depth simultaneously. The IPSO method is capable of improving the global search of particles in all of the search fields. Finding the optimum solution is adjusted by an inertia weight and acceleration coefficients. Here, we have examined the ability of the IPSO inversion by the synthetic gravity data due to a sedimentary basin, with and without noise. The calculated depth and gravity of the synthetic model do not differ too much from assumed values due to set limits for model parameters and are always within the range. Also, the mentioned method has been applied for the 2.5-D gravity inverse modelling of a sedimentary basin in Iran. We also have modelled the sedimentary basin in 2-D along seven profiles. Furthermore, using the depth values estimated by IPSO from all profiles, a 3-D model of the sedimentary basin was generated. The obtained maximum depth for this sedimentary basin is 2.62 km.

Evaluation of cloud computing resource scheduling based on improved optimization algorithm

Cloud computing, as a new computing mode in recent years, has been pursued by many users who have computational requirements, and the service quality of cloud computing depends largely on the efficiency of resource scheduling. In this study, an improved particle swarm optimization (IPSO) algorithm was proposed to improve the efficiency of resource scheduling, and simulation experiments were carried out on the IPSO algorithm and the traditional particle swarm optimization using CloudSim simulation platform. The phenomenon of premature appeared with the increase of the number of iterations, and the globally optimal solution was not found. The IPSO algorithm was more efficient in exploring the globally optimal solution, and the phenomenon of premature did not appear. As the number of tasks increased, the operation time of both algorithms increased, but the IPSO algorithm increased more slowly. The IPSO algorithm had more advantages when there were a large amount of tasks. Virtual machines in the two algorithms had different loads, and the load of the virtual machine in the IPSO algorithm was more balanced.

ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.