scholarly journals Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 122 ◽  
Author(s):  
Maheen Zahid ◽  
Fahad Ahmed ◽  
Nadeem Javaid ◽  
Raza Abbasi ◽  
Hafiza Zainab Kazmi ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Support Vector Regression ◽  
Smart Grids ◽  
Performance Metrics ◽  
Load Forecasting ◽  
Electricity Price ◽  
Support Vector ◽  
Price Forecasting ◽  
Electricity Load

Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. Forecasting about electricity load and price provides future trends and patterns of consumption. There is a loss in generation and use of electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead electricity price and load forecasting are beneficial for both suppliers and consumers. In this paper, Deep Learning (DL) and data mining techniques are used for electricity load and price forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature Elimination (RFE) and Random Forest (RF) are used for feature selection and feature extraction. Enhanced Convolutional Neural Network (ECNN) and Enhanced Support Vector Regression (ESVR) are used as classifiers. Grid Search (GS) is used for tuning of the parameters of classifiers to increase their performance. The risk of over-fitting is mitigated by adding multiple layers in ECNN. Finally, the proposed models are compared with different benchmark schemes for stability analysis. The performance metrics MSE, RMSE, MAE, and MAPE are used to evaluate the performance of the proposed models. The experimental results show that the proposed models outperformed other benchmark schemes. ECNN performed well with threshold 0.08 for load forecasting. While ESVR performed better with threshold value 0.15 for price forecasting. ECNN achieved almost 2% better accuracy than CNN. Furthermore, ESVR achieved almost 1% better accuracy than the existing scheme (SVR).

scholarly journals A hybrid technique based on convolutional neural network and support vector regression for intelligent diagnosis of rotating machinery

2017 ◽  
Vol 9 (6) ◽  
pp. 168781401770414 ◽  
Author(s):  
Wei You ◽  
Changqing Shen ◽  
Xiaojie Guo ◽  
Xingxing Jiang ◽  
Juanjuan Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Support Vector Regression ◽  
Rotating Machinery ◽  
Support Vector ◽  
Hybrid Technique ◽  
Intelligent Diagnosis

Short Term Electricity Price Forecasting Through Convolutional Neural Network (CNN)

2020 ◽  
pp. 1181-1188
Author(s):  
Zahoor Ali Khan ◽  
Sahiba Fareed ◽  
Mubbashra Anwar ◽  
Afrah Naeem ◽  
Hira Gul ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Electricity Price Forecasting

Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting

2016 ◽  
pp. 1161-1183 ◽  
Author(s):  
Tuncay Ozcan ◽  
Tarik Küçükdeniz ◽  
Funda Hatice Sezgin
Keyword(s):  
Support Vector Regression ◽  
Electricity Market ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Grey Model ◽  
Support Vector ◽  
Forecasting Model ◽  
Data Sets ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is crucial for electricity generation companies, distributors and other electricity market participants. In this study, several forecasting techniques are applied to time series modeling and forecasting of the hourly loads. Seasonal grey model, support vector regression, random forests, seasonal ARIMA and linear regression are benchmarked on seven data sets. A rolling forecasting model is developed and 24 hours of the next day is predicted for the last 14 days of each data set. This day-ahead forecasting model is especially important in day-ahead market activities and plant scheduling operations. Experimental results indicate that support vector regression and seasonal grey model outperforms other approaches in terms of forecast accuracy for day-ahead load forecasting.

Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR)

2020 ◽  
Vol 588 ◽  
pp. 125033 ◽  
Author(s):  
Mahdi Panahi ◽  
Nitheshnirmal Sadhasivam ◽  
Hamid Reza Pourghasemi ◽  
Fatemeh Rezaie ◽  
Saro Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Support Vector Regression ◽  
Spatial Prediction ◽  
Groundwater Potential ◽  
Support Vector ◽  
Potential Mapping ◽  
Groundwater Potential Mapping

scholarly journals Utilização de técnicas de Machine Learning e de Deep Learning para a predição de casos de internações causadas por dengue em municípios da Paraíba

2021 ◽  
Author(s):  
Ewerthon Dyego de Araújo Batista ◽  
Wellington Candeia de Araújo ◽  
Romeryto Vieira Lira ◽  
Laryssa Izabel de Araújo Batista
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Support Vector Regression ◽  
Multilayer Perceptron ◽  
Support Vector

Dengue é um problema de saúde pública no Brasil, os casos da doença voltaram a crescer na Paraíba. O boletim epidemiológico da Paraíba, divulgado em agosto de 2021, informa um aumento de 53% de casos em relação ao ano anterior. Técnicas de Machine Learning (ML) e de Deep Learning estão sendo utilizadas como ferramentas para a predição da doença e suporte ao seu combate. Por meio das técnicas Random Forest (RF), Support Vector Regression (SVR), Multilayer Perceptron (MLP), Long ShortTerm Memory (LSTM) e Convolutional Neural Network (CNN), este artigo apresenta um sistema capaz de realizar previsões de internações causadas por dengue para as cidades Bayeux, Cabedelo, João Pessoa e Santa Rita. O sistema conseguiu realizar previsões para Bayeux com taxa de erro 0,5290, já em Cabedelo o erro foi 0,92742, João Pessoa 9,55288 e Santa Rita 0,74551.

scholarly journals Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

2020 ◽  
Vol 12 (17) ◽  
pp. 7076 ◽  
Author(s):  
Arash Moradzadeh ◽  
Sahar Zakeri ◽  
Maryam Shoaran ◽  
Behnam Mohammadi-Ivatloo ◽  
Fazel Mohammadi
Keyword(s):  
Support Vector Regression ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Short Term Load Forecasting ◽  
Long Short Term Memory ◽  
Input Variables

Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target variables. Identifying the behavioral patterns of input variables as well as modeling their behavior in short-term periods of time are the major capabilities of the hybrid SVR-LSTM model. To present the efficiency of the suggested method, the conventional SVR and LSTM models are also applied to the used data. The results of the load forecasts by each network are evaluated using various statistical performance metrics. The obtained results show that the SVR-LSTM model with the highest correlation coefficient, i.e., 0.9901, is able to provide better results than SVR and LSTM, which have the values of 0.9770 and 0.9809, respectively. Finally, the results are compared with the results of other studies in this field, which continued to emphasize the superiority of the SVR-LSTM model.

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