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.

scholarly journals A comparative analysis of artificial neural network architectures for building energy consumption forecasting

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771987761 ◽  
Author(s):  
Jihoon Moon ◽  
Sungwoo Park ◽  
Seungmin Rho ◽  
Eenjun Hwang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Energy Management ◽  
Smart Grids ◽  
Load Forecasting ◽  
Energy Management Strategy ◽  
Forecasting Models ◽  
Artificial Neural

Smart grids have recently attracted increasing attention because of their reliability, flexibility, sustainability, and efficiency. A typical smart grid consists of diverse components such as smart meters, energy management systems, energy storage systems, and renewable energy resources. In particular, to make an effective energy management strategy for the energy management system, accurate load forecasting is necessary. Recently, artificial neural network–based load forecasting models with good performance have been proposed. For accurate load forecasting, it is critical to determine effective hyperparameters of neural networks, which is a complex and time-consuming task. Among these parameters, the type of activation function and the number of hidden layers are critical in the performance of neural networks. In this study, we construct diverse artificial neural network–based building electric energy consumption forecasting models using different combinations of the two hyperparameters and compare their performance. Experimental results indicate that neural networks with scaled exponential linear units and five hidden layers exhibit better performance, on average than other forecasting models.

scholarly journals Operational Probabilistic Forecasting of Coastal Freak Waves by Using an Artificial Neural Network

2020 ◽  
Vol 8 (3) ◽  
pp. 165
Author(s):  
Dong-Jiing Doong ◽  
Shien-Tsung Chen ◽  
Ying-Chih Chen ◽  
Cheng-Han Tsai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Freak Waves ◽  
Forecasting Models ◽  
Artificial Neural

Coastal freak waves (CFWs) are unpredictable large waves that occur suddenly in coastal areas and have been reported to cause casualties worldwide. CFW forecasting is difficult because the complex mechanisms that cause CFWs are not well understood. This study proposes a probabilistic CFW forecasting model that is an advance on the basis of a previously proposed deterministic CFW forecasting model. This study also develops a probabilistic forecasting scheme to make an artificial neural network model achieve the probabilistic CFW forecasting. Eight wave and meteorological variables that are physically related to CFW occurrence were used as the inputs for the artificial neural network model. Two forecasting models were developed for these inputs. Model I adopted buoy observations, whereas Model II used wave model simulation data. CFW accidents in the coastal areas of northeast Taiwan were used to calibrate and validate the model. The probabilistic CFW forecasting model can perform predictions every 6 h with lead times of 12 and 24 h. The validation results demonstrated that Model I outperformed Model II regarding accuracy and recall. In 2018, the developed CFW forecasting models were investigated in operational mode in the Operational Forecast System of the Taiwan Central Weather Bureau. Comparing the probabilistic forecasting results with swell information and actual CFW occurrences demonstrated the effectiveness of the proposed probabilistic CFW forecasting model.

scholarly journals FLOOD FORECASTING MODEL USING THE COMBINATION APPROACH

2020 ◽  
Vol 1 (2) ◽  
pp. 59-64
Author(s):  
Hu Weighuo ◽  
Hu He
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Flood Forecasting ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Highly Efficient ◽  
Artificial Neural ◽  
Combination Approach

This paper reviews the qualities of a good flood forecasting model such as timeliness, accuracy, and reliability. The article reviews the current forecasting models which are based on fuzzy logic, artificial neural network, as well as combination. The combination approach is gaining popularity and is found to be more flexible, accurate, reliable, and highly efficient in terms of development and output.

Evaluating ARIMA-Neural Network Hybrid Model Performance in Forecasting Stationary Timeseries

2013 ◽  
Vol 845 ◽  
pp. 510-515
Author(s):  
Seyed Navid Seyedi ◽  
Pouyan Rezvan ◽  
Saeed Akbarnatajbisheh ◽  
Syed Ahmad Helmi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Model Performance ◽  
Stationary Time Series ◽  
Forecasting Model ◽  
Dynamic Nature ◽  
Demand Prediction ◽  
Forecasting Models ◽  
Artificial Neural Network Ann

Demand prediction is one of most sophisticated steps in planning and investments. Although many studies are conducted to find the appropriate forecasting models, dynamic nature of forecasted parameters and their effecting factors are apparent evidences for continuous researches. ARIMA, Artificial Neural Network (ANN), and ARIMA-ANN hybrid model are well-known forecasting models. Many researchers concluded that the Hybrid model is the predominant forecasting model in comparison with ARIMA and ANN individual models. Most of these researches are based on non-stationary or seasonal timeseries, whereas in this article, hybrid models forecast ability by stationary time series is studied. Some following demand time steps from a paint manufacturing company are forecasted by all previously mentioned models and ARIMA-ANN hybrid model fails to present the best forecasts.

scholarly journals A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid

2015 ◽  
Vol 5 (4) ◽  
pp. 1756-1772 ◽  
Author(s):  
Ashfaq Ahmad ◽  
Nadeem Javaid ◽  
Nabil Alrajeh ◽  
Zahoor Khan ◽  
Umar Qasim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Smart Grid ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Artificial Neural

scholarly journals The Impact of the Selection of Exogenous Variables in the ANFIS Model on the Results of the Daily Load Forecast in the Power Company

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 345
Author(s):  
Janusz Sowinski
Keyword(s):  
Load Forecasting ◽  
Large Set ◽  
Exogenous Variables ◽  
Anfis Model ◽  
Load Forecast ◽  
Forecasting Models ◽  
Power Company ◽  
Ex Post ◽  
Daily Load ◽  
Selection Of

Forecasting of daily loads is crucial for the Distribution System Operators (DSO). Contemporary short-term load forecasting models (STLF) are very well recognized and described in numerous articles. One of such models is the Adaptive Neuro-Fuzzy Inference System (ANFIS), which requires a large set of historical data. A well-recognized issue both for the ANFIS and other daily load forecasting models is the selection of exogenous variables. This article attempts to verify the statement that an appropriate selection of exogenous variables of the ANFIS model affects the accuracy of the forecasts obtained ex post. This proposal seems to be a return to the roots of the Polish econometrics school and the use of the Hellwig method to select exogenous variables of the ANFIS model. In this context, it is also worth asking whether the use of the Hellwig method in conjunction with the ANFIS model makes it possible to investigate the significance of weather variables on the profile of the daily load in an energy company. The functioning of the ANFIS model was tested for some consumers exhibiting high load randomness located within the area under supervision of the examined power company. The load curves featuring seasonal variability and weekly similarity are suitable for forecasting with the ANFIS model. The Hellwig method has been used to select exogenous variables in the ANFIS model. The optimal set of variables has been determined on the basis of integral indicators of information capacity H. Including an additional variable, i.e., air temperature, has also been taken into consideration. Some results of ex post daily load forecast are presented.

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