Small-Scale Building Load Forecast based on Hybrid Forecast Engine

Mohammadi, Mohsen; Talebpour, Faraz; Safaee, Esmaeil; Ghadimi, Noradin; Abedinia, Oveis

doi:10.1007/s11063-017-9723-2

Cited by 115 publications

(47 citation statements)

References 30 publications

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“…The recommended approach was compared with various well‐known forecasting models such as the RBFNN, the support vector machine (SVM), the combined wavelet transformed with RBFNN (WT + RBFNN), the persistence model, the autoregressive integrated moving average (ARIMA), and the combined EMD with RBFNN (EMD + RBFNN). By partial comparison, the same condition was applied in all models through this approach, and the same error criteria were considered as root mean square error (RMSE), mean absolute percentage error (MAPE), and normalized mean absolute error (NMAE), which can be introduced as follows:

italicRMSE = {[\frac{1}{N} {false\sum}_{t = 1}^{N} {((), S_{italicACT ((), t)} - S_{italicFOR ((), t)})}^{2}]}^{1 / 2},

italicMAPE = \frac{1}{N} \sum_{t = 1}^{N} \frac{|S_{ACT (t)} - S_{FOR (t)}|}{S_{ACT (t)}} \times 100,

italicNMAE = \frac{1}{N} \sum_{t = 1}^{N} \frac{|S_{ACT (t)} - S_{FOR (t)}|}{S_{N}} \times 100 .

…”

Section: Numerical Resultsmentioning

confidence: 99%

Integrated bidding strategy of distributed energy resources based on novel prediction and market model

Lin

Gauzily

2020

Int J Energy Res

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Summary This paper presents an integrated operation model of renewable energy, distributed generators (DGs), energy storage, and demand response to overcome the challenges of renewable energy market participation. Additionally, an accurate prediction model for wind, photovoltaic (PV) power, and market price is introduced based on ensemble empirical model decomposition. In this model, radial basis function neural network (RBFNN) is used for the forecasting of subsignals from the analysis model. The training model of RBFNN is shaped based on the B‐water cycle algorithm (WCA) optimization method. Additionally, by considering the adjusted market, demand response, and uncertainties analysis, this method reduced the economic damages of generators and loads. A case study test has consisted of a wind turbine, photovoltaic energy, fuel cell energy, demand response, and energy storage.

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italicRMSE = {[\frac{1}{N} {false\sum}_{t = 1}^{N} {((), S_{italicACT ((), t)} - S_{italicFOR ((), t)})}^{2}]}^{1 / 2},

italicMAPE = \frac{1}{N} \sum_{t = 1}^{N} \frac{|S_{ACT (t)} - S_{FOR (t)}|}{S_{ACT (t)}} \times 100,

italicNMAE = \frac{1}{N} \sum_{t = 1}^{N} \frac{|S_{ACT (t)} - S_{FOR (t)}|}{S_{N}} \times 100 .

…”

Section: Numerical Resultsmentioning

confidence: 99%

Integrated bidding strategy of distributed energy resources based on novel prediction and market model

Lin

Gauzily

2020

Int J Energy Res

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“…Multitude researches have investigated the microgrid operation with various [4][5][6] distributed generations (DGs) Optimized coordinated power dispatch approach for a microgrid (MG) scheduling 7 Optimal method with high robustness for MG 8 Stochastic planning scheme for 24 h scheduling for an MG 9 Optimization method for reduction of whole expenditure in an MG 8 Multiobjective optimization methods by assuming expenditure, emission, etc 10,11 Multiobjective optimization for economic dispatch for microgrid with reliability 12 Management method for renewable sources 13 Hybrid-integer nonlinear approach for MG scheduling for achieving upper photovoltaic power 14 Smart power management of microgrid using artificial intelligence methods 15 Cost-effective combined heat and power dispatch with heat/energy reliance features [16][17][18][19] Demand response (DR) problem that can optimize the scheduling using optimization algorithm 20 DR program on stochastic power provided to big users by green resources 21,22 Other models [23][24][25] The considered MO optimization is handled in this work using an epsilon limitation approach. A fuzzy satisfying method is utilized for opting the best conciliation answer, and DRP is exerted to decrease the operation expenditure.…”

Section: Methods Referencementioning

confidence: 99%

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

Zeng

Berti

2019

Computational Intelligence

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Microgrids can be assumed as a solution model for green energy sources, energy storage systems, and combined heat and power (CHP) systems. In this work, the cost and emission minimization based on a demand response (DR) program is considered an optimization problem. To solve the mentioned problem a new multiobjective optimization algorithm (improved particle swarm optimization) is proposed based on a fuzzy mechanism to select the optimal value. The microgrid system includes two CHP units, fuel cell and battery systems, and the heat buffer tank. In this problem, two different feasible operating regions have been assumed in CHPs. Accordingly, to decrease the operational cost, time‐of‐use, and real‐time pricing DR programs have been simulated, and the impacts of the mentioned models are evaluated overload profiles. The effectiveness of proposed models has been applied on different cases studies by different scenarios. The proposed model solved the DR program, time of use‐DR and real‐time pricing‐DR problems. The proposed model could reduce the cost about 10%.

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“…ANNs are kinds of computing systems that are inspired by biological neural networks. These systems learn activities by examining examples (in other words, they improve their performance in doing activities over time), and generally, this is done without any dedicated programming …”

Section: Tumor Classification Based On Mlp‐cwoa Classifiermentioning

confidence: 99%

“…These systems learn activities by examining examples (in other words, they improve their performance in doing activities over time), and generally, this is done without any dedicated programming. [51][52][53][54][55] In the field of mathematical modeling, RBF is an ANN that uses RBFs as activation functions. The output of this network is a linear combination of RBFs for input parameters and neurons.…”

Section: Tumor Classification Based On Mlp-cwoa Classifiermentioning

confidence: 99%

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

Gong

Gao

Abza

2019

Computational Intelligence

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Accurate and early detection of the brain tumor region has a great impact on the choice of treatment, its success rate, and the follow‐up of the disease process over time. This study presents a new bioinspired technique for the early detection of the brain tumor area to improve the chance of completely healing. The study presents a multistep technique to detect the brain tumor area. Herein, after image preprocessing and image feature extraction, an artificial neural network is used to determine the tumor area in the image. The method is based on using an improved version of the whale optimization algorithm for optimal selection of the features and optimizing the artificial neural network weights for classification. Simulation results of the proposed method are applied to FLAIR, T1, and T2 datasets and are compared with different algorithms. Three performance indexes including correct detection rate, false acceptance rate, and false rejection rate are selected for the system performance analysis. Final results showed the superiority of the proposed method toward the other similar methods.

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Small-Scale Building Load Forecast based on Hybrid Forecast Engine

Cited by 115 publications

References 30 publications

Integrated bidding strategy of distributed energy resources based on novel prediction and market model

Integrated bidding strategy of distributed energy resources based on novel prediction and market model

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

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