Application of SVR optimized by Modified Simulated Annealing (MSA-SVR) air conditioning load prediction model

2019 ◽  
Vol 15 ◽  
pp. 247-251 ◽  
Author(s):  
Yuxin Tao ◽  
Hairong Yan ◽  
Hang Gao ◽  
Yuying Sun ◽  
Gang Li
Keyword(s):  
Simulated Annealing ◽  
Prediction Model ◽  
Air Conditioning ◽  
Load Prediction

Air Conditioning Load Prediction of an Office Building based on Long Short Term Memory Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Mengxiang Zhuang ◽  
Qixin Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Air Conditioning ◽  
Short Term Memory ◽  
Load Prediction ◽  
Short Term ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Long Short Term Memory ◽  
Grey Relational

Background: Energy conservation has always been a major issue in our country, and the air conditioning energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load can be predicted and the air conditioning equipment can respond in advance, it can not only save energy, but also extend the life of the equipment. Introduction: The Neural network proposed in this paper can deeply analyze the load characteristics through three gate structures, which is helpful to improve the prediction accuracy. Combined with grey relational degree method, the prediction speed can be accelerated. Method: This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and selects the best ones. A Long Short Term Memory Neural Network (LSTMNN) prediction model was established. In this paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and the predicted results are compared with the real values. Results: Compared with Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine (SVM) prediction model, the simulation results show that this method has better effect on air conditioning load prediction. Conclusion: Grey relational degree analysis can extract the main factors from the numerous data, which is more convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air conditioning load on time series, but also considers the nonlinear relation between load and other factors. This model has higher prediction accuracy, shorter prediction time and great application potential.

A Nonlinear Autoregressive With Exogenous Inputs Artificial Neural Network Model for Building Thermal Load Prediction

2019 ◽  
Vol 142 (5) ◽  
Author(s):  
Byeongho Yu ◽  
Dongsu Kim ◽  
Heejin Cho ◽  
Pedro Mago
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Nonlinear System ◽  
Prediction Model ◽  
Thermal Load ◽  
Ann Model ◽  
Load Prediction ◽  
Artificial Neural ◽  
Nonlinear Autoregressive

Abstract Thermal load prediction is a key part of energy system management and control in buildings, and its accuracy plays a critical role to improve building energy performance and efficiency. Regarding thermal load prediction, various types of prediction model have been considered and studied, such as physics-based, statistical, and machine learning models. Physical models can be accurate but require extended lead time for model development. Statistical models are relatively simple to develop and require less computation time, but they may not provide accurate results for complex energy systems with intricate nonlinear dynamic behaviors. This study proposes an artificial neural network (ANN) model, one of the prevalent machine learning methods to predict building thermal load, combining with the concept of nonlinear autoregressive with exogenous inputs (NARX). NARX-ANN prediction model is distinguished from typical ANN models because the NARX concept can address nonlinear system behaviors effectively based on its recurrent architectures and time indexing features. To examine the suitability and validity of NARX-ANN model for building thermal load prediction, a case study is carried out using the field data of an academic campus building at Mississippi State University (MSU). Results show that the proposed NARX-ANN model can provide an accurate and robust prediction performance and effectively address nonlinear system behaviors in the prediction.

scholarly journals Research on Short-Term Load Prediction Based on Seq2seq Model

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3199 ◽  
Author(s):  
Gangjun Gong ◽  
Xiaonan An ◽  
Nawaraj Kumar Mahato ◽  
Shuyan Sun ◽  
Si Chen ◽  
...  
Keyword(s):  
Power System ◽  
Prediction Model ◽  
Short Term Memory ◽  
Predictive Performance ◽  
Weather Conditions ◽  
Attention Mechanism ◽  
Load Prediction ◽  
Short Term ◽  
System Load ◽  
The Impact

Electricity load prediction is the primary basis on which power-related departments to make logical and effective generation plans and scientific scheduling plans for the most effective power utilization. The perpetual evolution of deep learning has recommended advanced and innovative concepts for short-term load prediction. Taking into consideration the time and nonlinear characteristics of power system load data and further considering the impact of historical and future information on the current state, this paper proposes a Seq2seq short-term load prediction model based on a long short-term memory network (LSTM). Firstly, the periodic fluctuation characteristics of users’ load data are analyzed, establishing a correlation of the load data so as to determine the model’s order in the time series. Secondly, the specifications of the Seq2seq model are given preference and a coalescence of the Residual mechanism (Residual) and the two Attention mechanisms (Attention) is developed. Then, comparing the predictive performance of the model under different types of Attention mechanism, this paper finally adopts the Seq2seq short-term load prediction model of Residual LSTM and the Bahdanau Attention mechanism. Eventually, the prediction model obtains better results when merging the actual power system load data of a certain place. In order to validate the developed model, the Seq2seq was compared with recurrent neural network (RNN), LSTM, and gated recurrent unit (GRU) algorithms. Last but not least, the performance indices were calculated. when training and testing the model with power system load data, it was noted that the root mean square error (RMSE) of Seq2seq was decreased by 6.61%, 16.95%, and 7.80% compared with RNN, LSTM, and GRU, respectively. In addition, a supplementary case study was carried out using data for a small power system considering different weather conditions and user behaviors in order to confirm the applicability and stability of the proposed model. The Seq2seq model for short-term load prediction can be reported to demonstrate superiority in all areas, exhibiting better prediction and stable performance.

TAR Based Hotspot Prediction in Cloud Data Centres

2019 ◽  
Vol 11 (3) ◽  
pp. 1-22
Author(s):  
Anu Valiyaparambil Raveendran ◽  
Elizabeth Sherly Sherly
Keyword(s):  
Prediction Model ◽  
Resource Utilization ◽  
Data Centers ◽  
Linear Regression Analysis ◽  
Load Prediction ◽  
Bandwidth Utilization ◽  
Virtual Machine Migration ◽  
Cloud Data ◽  
Network Bandwidth ◽  
Cloud Data Centers

In this article, the authors studied hotspots in cloud data centers, which are caused due to a lack of resources to satisfy the peak immediate requests from clients. The nature of resource utilization in cloud data centers are totally dynamic in context and may lead to hotspots. Hotspots are unfavorable situations which cause SLA violations in some scenarios. Here they use trend aware regression (TAR) methods as a load prediction model and perform linear regression analysis to detect the formation of hotspots in physical servers of cloud data centers. This prediction model provides an alarm period for the cloud administrators either to provide enough resources to avoid hotspot situations or perform interference aware virtual machine migration to balance the load on servers. Here they analyzed the physical server resource utilization model in terms of CPU utilization, memory utilization and network bandwidth utilization. In the TAR model, the authors consider the degree of variation between the current points in the prediction window to forecast the future points. The TAR model provides accurate results in its predictions.

Optimization of Financial Forecasting Based on Least Squares Support Vector Machine

2013 ◽  
Vol 760-762 ◽  
pp. 1987-1991
Author(s):  
Yun Fa Li
Keyword(s):  
Support Vector Machine ◽  
Simulated Annealing ◽  
Stock Market ◽  
Prediction Model ◽  
Least Squares ◽  
Simulated Annealing Algorithm ◽  
Least Square ◽  
Support Vector ◽  
Stock Investment ◽  
Annealing Algorithm

To master the variation regularity of finance, obtain greater benefits in stock investment. study of the support vector machine and application in prediction of stock market. The simulated annealing algorithm to optimize the least squares support vector machine prediction model, and the least square support vector machine and simulated annealing algorithm is described, given the optimal prediction model. Through the research on the simulation of the Hang Seng Index, shows that this method is simple, fast convergence, the algorithm with high accuracy. Has the actual guiding sense for investors, the stock market of the financial firm to operate.

Sign in / Sign up

Export Citation Format

Share Document