Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach

Jung, Hyun Chul; Kim, Jin Sung; Heo, Hoon

doi:10.1016/j.enbuild.2014.12.029

Cited by 100 publications

(42 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Principles of an ANN. There are various machine learning algorithms that have been effectively applied to the prediction of properties for energy systems, such as ANN [12,13,17,18,20,38], SVM [20,39,40], and ELM [21,41]. Because the ANN method is the most popular algorithm for numerical predictions [42], we only introduce the basic principle of ANN here.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Liu

et al. 2017

International Journal of Photoenergy

View full text Add to dashboard Cite

Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a highperformance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

show abstract

Section: Machine Learning Methodsmentioning

confidence: 99%

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Liu

et al. 2017

International Journal of Photoenergy

View full text Add to dashboard Cite

show abstract

“…Since related data such as temperature and humidity has nonlinear patterns, the prediction method must be applicable for nonlinearity. As ARIMA (Autoregressive Integrated Moving Average) model, Fourier series and regression analysis are appropriate for linear data, these means of analysis are inadequate to expect high prediction level in forecasting electricity consumption [17]. On the other hand, machine learning is a method to analyze nonlinear patterned data.…”

Section: Literature Review On Energy Consumption Prediction Methodsmentioning

confidence: 99%

Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR

2018

View full text Add to dashboard Cite

Abstract:In many countries, DR (Demand Response) has been developed for which customers are motivated to save electricity by themselves during peak time to prevent grand-scale blackouts. One of the common methods in DR, is CPP (Critical Peak Pricing). Predicting energy consumption is recognized as one of the tool for dealing with CPP. There are a variety of studies in developing the model of energy consumption, which is based on energy simulation, data-driven model or metamodelling. However, it is difficult for general users to use these models due to requirement of various sensing data and expertise. And it also takes long time to simulate the models. These limitations can be an obstacle for achieving CPP's purpose that encourages general users to manage their energy usage by themselves. As an alternative, this research suggests to use open data and GA (Genetic Algorithm)-SVR (Support Vector Regression). The model is applied to a hospital in Korea and 34,636 data sets (1 year) are collected while 31,756 (11 months) sets are used for training and 2880 sets (1 month) are used for validation. As a result, the performance of proposed model is 14.17% in CV (RMSE), which satisfies the Korea Energy Agency's and ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) error allowance range of ±30%, and ±20% respectively.

show abstract

“…By changing the inequality constraints in SVR into equality ones, the LSSVM method can avoid the long and computationally difficult convex quadratic programming and, thus, largely speeds up training. The LSSVM for regression is called LSSVR, which has been extended and applied to forecasting by many studies [25][26][27]. In this section, we briefly introduce the LSSVR for function estimation.…”

Section: Least Squares Support Vector Regression (Lssvr)mentioning

confidence: 99%

A Novel Covert Agent for Stealthy Attacks on Industrial Control Systems Using Least Squares Support Vector Regression

Xie

Wang

2018

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Research on stealthiness has become an important topic in the field of data integrity (DI) attacks. To construct stealthy DI attacks, a common assumption in most related studies is that attackers have prior model knowledge of physical systems. In this paper, such assumption is relaxed and a covert agent is proposed based on the least squares support vector regression (LSSVR). By estimating a plant model from control and sensory data, the LSSVR-based covert agent can closely imitate the behavior of the physical plant. Then, the covert agent is used to construct a covert loop, which can keep the controller's input and output both stealthy over a finite time window. Experiments have been carried out to show the effectiveness of the proposed method.

show abstract

Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach

Cited by 100 publications

References 39 publications

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR

A Novel Covert Agent for Stealthy Attacks on Industrial Control Systems Using Least Squares Support Vector Regression

Contact Info

Product

Resources

About