An Artificial Neural Network based Approach to Electric Demand Response Implementation

2018 ◽  
Author(s):  
Md. Kamruzzaman ◽  
Mohammed Benidris ◽  
Sesh Commuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Demand Response ◽  
Artificial Neural

scholarly journals Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3012 ◽  
Author(s):  
Nikos Kampelis ◽  
Elisavet Tsekeri ◽  
Dionysia Kolokotsa ◽  
Kostas Kalaitzakis ◽  
Daniela Isidori ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Energy Management ◽  
Demand Response ◽  
Fundamental Aspect ◽  
Baseline Scenario ◽  
Load Shifting ◽  
Pricing Scheme ◽  
Artificial Neural

Demand Response (DR) is a fundamental aspect of the smart grid concept, as it refers to the necessary open and transparent market framework linking energy costs to the actual grid operations. DR allows consumers to directly or indirectly participate in the markets where energy is being exchanged. One of the main challenges for engaging in DR is associated with the initial assessment of the potential rewards and risks under a given pricing scheme. In this paper, a Genetic Algorithm (GA) optimisation model, using Artificial Neural Network (ΑΝΝ) power predictions for day-ahead energy management at the building and district levels, is proposed. Individual building and building group analysis is conducted to evaluate ANN predictions and GA-generated solutions. ANN-based short term electric power forecasting is exploited in predicting day-ahead demand, and form a baseline scenario. GA optimisation is conducted to provide balanced load shifting and cost-of-energy solutions based on two alternate pricing schemes. Results demonstrate the effectiveness of this approach for assessing DR load shifting options based on a Time of Use pricing scheme. Through the analysis of the results, the practical benefits and limitations of the proposed approach are addressed.

Using Artificial Neural Network to Execute Demand Response Considering Indoor Thermal Comfort and Forecast Load-Shedding

2014 ◽  
Vol 716-717 ◽  
pp. 1399-1408
Author(s):  
Yung Chung Chang ◽  
Jyun Ting Lu ◽  
Yu Chung Liu ◽  
Chun Hong Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Comfort ◽  
Demand Response ◽  
Load Shedding ◽  
Cooling Load ◽  
Air Conditioning System ◽  
Indoor Thermal Comfort ◽  
Central Air Conditioning ◽  
Artificial Neural

This paper used artificial neural network to forecast the cooling load in the building in 24 hours. The unloading experiment kept the indoor thermal comfort at the ideal range of PMV=0~0.5 and PPD=5~10. Finally, dry bulb temperature, relative humidity, wet-bulb temperature and forecast cooling load were used for modeling by neural network. We can use this model to forecast how much load can be unloaded in summer peak hours accurately. This method controls the demand response for central air conditioning system, not only maintaining comfortable indoor environment, but also attaining the goals for reducing the electric expenses.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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