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This paper evaluates the performance of four artificial intelligence algorithms for building energy consumption prediction. The backward propagation neural network (BPNN), support vector regression (SVR), adaptive network-based fuzzy inference system (ANFIS) and extreme learning machine (ELM) methods are reviewed and their performances for predicting building energy consumption are compared. A selection...
In this paper, Extreme Learning Machine (ELM) is demonstrated to be a powerful tool for electricity consumption prediction based on its competitive prediction accuracy and superior computational speed compared to Support Vector Machine (SVM). Moreover, ELM is utilized to investigate the potentials of using auxiliary information such as electricity-related factors and environmental factors to augment...
In this paper, we focus on the prediction method of building energy consumption time series. The building energy consumption data can be regarded as a time series, which is usually nonlinear and non-stationary. Traditional time series analysis model has lower prediction accuracy. Then the machine learning method, especially support vector regression algorithm always has better performance to deal...
In this paper, a unified decision support system (DSS) is proposed, which uses real-time energy measurements and process operational states to make effective decisions, enabling high-performance manufacturing. To reduce the number of required sensors and amount of logged data, our proposed DSS includes an intelligent framework which identifies the process operational states based on energy measurements...
Forecasting is a tool to predict the future event with the uncertainty and depending on the historical data. It is important for an upcoming planning event because the forecasting result will deliver the initial view for the future. This paper reviews the Least Square Support Vector Machine (LSSVM) and Group Method of Data Handling (GMDH) used in different application of forecasting. Besides, this...
Support vector machines (SVM) is a widely used method which can treat problems involving small sample, devilish learning, and high dimension. The current paper conduct a multivariate SVM in a total-factor production framework, and the GDP per capita, capital stock and labor are taken as the independent variables and the energy consumption is the dependent variable. The Gaussian radial basis function...
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