scholarly journals A Parallel Evolutionary Computing-Embodied Artificial Neural Network Applied to Non-Intrusive Load Monitoring for Demand-Side Management in a Smart Home: Towards Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1649 ◽  
Author(s):  
Yu-Hsiu Lin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Execution Time ◽  
Smart Home ◽  
Evolutionary Computing ◽  
Demand Side Management ◽  
Demand Side ◽  
Training Samples ◽  
Load Monitoring ◽  
Parallel Ga

Non-intrusive load monitoring (NILM) is a cost-effective approach that electrical appliances are identified from aggregated whole-field electrical signals, according to their extracted electrical characteristics, with no need to intrusively deploy smart power meters (power plugs) installed for individual monitored electrical appliances in a practical field of interest. This work addresses NILM by a parallel Genetic Algorithm (GA)-embodied Artificial Neural Network (ANN) for Demand-Side Management (DSM) in a smart home. An ANN’s performance in terms of classification accuracy depends on its training algorithm. Additionally, training an ANN/deep NN learning from massive training samples is extremely computationally intensive. Therefore, in this work, a parallel GA has been conducted and used to integrate meta-heuristics (evolutionary computing) with an ANN (neurocomputing) considering its evolution in a parallel execution relating to load disaggregation in a Home Energy Management System (HEMS) deployed in a real residential field. The parallel GA that involves iterations to excessively cost its execution time for evolving an ANN learning model from massive training samples to NILM in the HEMS and works in a divide-and-conquer manner that can exploit massively parallel computing for evolving an ANN and, thus, reduce execution time drastically. This work confirms the feasibility and effectiveness of the parallel GA-embodied ANN applied to NILM in the HEMS for DSM.

scholarly journals Short-Term Load Forecasting in Smart Grids: An Intelligent Modular Approach

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 164 ◽  
Author(s):  
Ashfaq Ahmad ◽  
Nadeem Javaid ◽  
Abdul Mateen ◽  
Muhammad Awais ◽  
Zahoor Ali Khan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Execution Time ◽  
Smart Grids ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Forecasting Model ◽  
Exogenous Variables ◽  
Forecasting Models ◽  
The Third

Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability.

A Smart Home Energy Management Strategy Based on Demand Side Management

2016 ◽  
Author(s):  
Zafar Iqbal ◽  
Nadeem Javaid ◽  
Mobushir Riaz Khan ◽  
Farman Ali Khan ◽  
Zahoor Ali Khan ◽  
...  
Keyword(s):  
Energy Management ◽  
Smart Home ◽  
Management Strategy ◽  
Demand Side Management ◽  
Demand Side ◽  
Energy Management Strategy ◽  
On Demand ◽  
Home Energy Management

An Artificial Neural Network Recognition Method for Single-Phase Earth Fault of Ultra-High Voltage Power Grid

2013 ◽  
Vol 333-335 ◽  
pp. 1659-1662
Author(s):  
Hai Wei Lu ◽  
Gang Wu ◽  
Chao Xiong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Single Phase ◽  
Training Sample ◽  
Normal Operation ◽  
Recognition Method ◽  
Time Motion ◽  
Training Samples ◽  
Artificial Neural ◽  
Network Diagnosis

Fault diagnosis is very important to make the system return to normal operation quickly after an accident. This paper diagnoses the specific component failure and failure area when the real-time motion information of inputting protection and switch transferred to a trained artificial neural network model by building an artificial neural network diagnosis model of components such as transmission line, bus bar and transformer, training the artificial neural network through taking the failure rule which is found by the historic fault data as a training sample. This method has obvious advantages in the accuracy and speed of diagnosis compared with the previous artificial neural network and overcomes the shortcomings of the incompletion of training samples and not well dealing with the heuristic knowledge.

Study on Load Monitoring and Demand Side Management Strategy of Chemical Enterprise

2021 ◽  
Vol 55 (6) ◽  
pp. 534-545
Author(s):  
Sang Yingjun ◽  
Sui Tingyu ◽  
Peng Kang ◽  
Ding Zizhen ◽  
Zhao Xuan ◽  
...  
Keyword(s):  
Management Strategy ◽  
Demand Side Management ◽  
Demand Side ◽  
Load Monitoring

Predictions of vertical train-bridge response using artificial neural network-based surrogate model

2019 ◽  
Vol 22 (12) ◽  
pp. 2712-2723 ◽  
Author(s):  
Xu Han ◽  
Huoyue Xiang ◽  
Yongle Li ◽  
Yichao Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Dynamic Systems ◽  
Artificial Neural Network Model ◽  
Stochastic Excitations ◽  
Training Samples ◽  
Artificial Neural ◽  
Nonlinear Autoregressive

To improve the efficiency of reliability calculations for vehicle-bridge systems, we present a surrogate modeling method based on a nonlinear autoregressive with exogenous input artificial neural network model and an important sample, which can forecast responses of dynamic systems, such as vehicle-bridge systems, subjected to stochastic excitations. We also propose a process to analyze the method. A quarter-vehicle model is used to verify the proposed method’s precision, and the nonlinear autoregressive with exogenous input artificial neural network model is used to predict responses of vertical vehicle-bridge systems. The results show that, compared to other training samples, the nonlinear autoregressive with exogenous input artificial neural network model has better prediction accuracy when the sample with the maximum response is considered as an important sample and is used to train the nonlinear autoregressive with exogenous input artificial neural network model, and it requires only two-time numerical simulation (or Monte Carlo simulation) at most, which is used in the training of the nonlinear autoregressive with exogenous input artificial neural network model.

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