Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks

Xia, Changliang; Wang, Jian; McMenemy, Karen

doi:10.1016/j.ijepes.2010.01.009

Cited by 186 publications

(91 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Artificial Neural Network (ANN) recently became highly popular for energy management in the built environment, which is highly complex and nonlinear [20,[36][37][38], primarily because of the strength of ANN in modelling complex systems. ANN mimics the biological neural system to find correlations for complex systems without having an explicit functional relationship [11].…”

Section: Artificial Neural Network For District Energy Managementmentioning

confidence: 99%

A Smart Forecasting Approach to District Energy Management

2017

View full text Add to dashboard Cite

Abstract:This study presents a model for district-level electricity demand forecasting using a set of Artificial Neural Networks (ANNs) (parallel ANNs) based on current energy loads and social parameters such as occupancy. A comprehensive sensitivity analysis is conducted to select the inputs of the ANN by considering external weather conditions, occupancy type, main income providers' employment status and related variables for the fuel poverty index. Moreover, a detailed parameter tuning is conducted using various configurations for each individual ANN. The study also demonstrates the strength of the parallel ANN models in different seasons of the years. In the proposed district level energy forecasting model, the training and testing stages of parallel ANNs utilise dataset of a group of six buildings. The aim of each individual ANN is to predict electricity consumption and the aggregated demand in sub-hourly time-steps. The inputs of each ANN are determined using Principal Component Analysis (PCA) and Multiple Regression Analysis (MRA) methods. The accuracy and consistency of ANN predictions are evaluated using Pearson coefficient and average percentage error, and against four seasons: winter, spring, summer, and autumn. The lowest prediction error for the aggregated demand is about 4.51% for winter season and the largest prediction error is found as 8.82% for spring season. The results demonstrate that peak demand can be predicted successfully, and utilised to forecast and provide demand-side flexibility to the aggregators for effective management of district energy systems.

show abstract

Section: Artificial Neural Network For District Energy Managementmentioning

confidence: 99%

A Smart Forecasting Approach to District Energy Management

2017

View full text Add to dashboard Cite

show abstract

“…Many forecasting methods, including artificial intelligence techniques, multivariate regression, and time series analysis, have frequently been applied to energy demand forecasting [5][6][7][8][9][10][11][12][13]. A large number of samples are required for multivariate regression and time series analysis like the autoregressive integrated moving average (ARIMA) [14].…”

Section: Introductionmentioning

confidence: 99%

Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting

2017

Sustainability

View full text Add to dashboard Cite

Abstract:Energy demand prediction plays an important role in sustainable development. The GM(1,1) model has drawn our attention to energy demand forecasting because it only needs a few data points to construct a time series model without statistical assumptions. Residual modification is often considered as well to improve the accuracy of predictions. Several residual modification models have been proposed, but they focused on residual sign estimation, whereas the FLNGM(1,1) model using functional-link net (FLN) can estimate the sign as well as the modification range for each predicted residual. However, in the original FLN, an activation function with an inner product assumes that criteria are independent of each other, so additivity might influence the forecasting performance of FLNGM(1,1). Therefore, in this study, we employ the FLN with a fuzzy integral instead of an inner product to propose a nonadditive FLNGM(1,1). Experimental results based on real energy demand cases demonstrate that the proposed grey prediction model performs well compared with other grey residual modification models that use sign estimation and the additive FLNGM(1,1).

show abstract

“…But these methods are not suitable for the prediction of dynamic load time series. With the development of artificial intelligence technology, many forecasting models based on intelligent theory are proposed and applied [6,7]. As an important branch of intelligent theory, neural network has been widely used in the STLF field because of its unique advantages.…”

Section: Introductionmentioning

confidence: 99%

Short‐Term Load Forecasting Model Based on the Fusion of PSRT and QCNN

Zhang¹

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Short-term load forecasting (STLF) model based on the fusion of Phase Space Reconstruction Theory (PSRT) and Quantum Chaotic Neural Networks (QCNN) was proposed. The quantum computation and chaotic mechanism were integrated into QCNN, which was composed of quantum neurons and chaotic neurons. QCNN has four layers, and they are the input layer, the first hidden layer of quantum hidden nodes, the second hidden layer of chaotic hidden nodes, and the output layer. The theoretical basis of constructing QCNN is Phase Space Reconstruction Theory (PSRT). Through the actual example simulation, the simulation results show that proposed model has good forecasting precision and stability.

show abstract

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks

Cited by 186 publications

References 19 publications

A Smart Forecasting Approach to District Energy Management

A Smart Forecasting Approach to District Energy Management

Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting

Short‐Term Load Forecasting Model Based on the Fusion of PSRT and QCNN

Contact Info

Product

Resources

About