Electricity demand forecasting based on feature extraction and optimized backpropagation neural network
As the global population is growing at a high rate, so is the electricity demand also increasing at a faster rate. This exerts pressure on electricity-generating plants and maintenance engineers because of the variability in demand. Avoiding disruption in the supply to meet demand requires forecasti...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671123001882 |
| _version_ | 1827581918163501056 |
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| author | Eric Ofori-Ntow Jnr Yao Yevenyo Ziggah |
| author_facet | Eric Ofori-Ntow Jnr Yao Yevenyo Ziggah |
| author_sort | Eric Ofori-Ntow Jnr |
| collection | DOAJ |
| description | As the global population is growing at a high rate, so is the electricity demand also increasing at a faster rate. This exerts pressure on electricity-generating plants and maintenance engineers because of the variability in demand. Avoiding disruption in the supply to meet demand requires forecasting what the future of demand will look like to be able to plan adequately towards it. This study, therefore, develops a new forecasting model using feature extraction (FE) where statistical information of the hourly demand data is extracted which serves as input variables for Backpropagation neural network (BPNN) optimized by particle swarm optimization (PSO) for electricity demand forecasting in Ghana. The model known as FE-PSO-BPNN is compared to other seven models such as Radial Basis Function (RBFNN), Random Forest (RF), Gradient Boosting Machine (GBM), Multivariate Adaptive Regression Splines (MARS), BPNN, and PSO-RBFNN where FE selects the input variables for all models. Electricity demand data from Ghana Grid Company from the period including 1st September 2018 to 30th November 2019 is used for the testing of the model's performance. Evaluation criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Scatter Index (SI) were used. The proposed model is more powerful in forecasting electricity demand than the others as it has RMSE (0.5344), MAE (3.3845), MAPE (0.1773), and SI (0.0003). The model is expected to be a better option for electricity sector managers when considering demand forecasting. |
| first_indexed | 2024-03-08T22:43:10Z |
| format | Article |
| id | doaj.art-45dfdd0e27f6438787c8a89a40425eb6 |
| institution | Directory Open Access Journal |
| issn | 2772-6711 |
| language | English |
| last_indexed | 2024-03-08T22:43:10Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj.art-45dfdd0e27f6438787c8a89a40425eb62023-12-17T06:43:16ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112023-12-016100293Electricity demand forecasting based on feature extraction and optimized backpropagation neural networkEric Ofori-Ntow Jnr0Yao Yevenyo Ziggah1Faculty of Engineering, University of Mines and Technology, P. O. Box 237, Tarkwa, Ghana; Corresponding author.Faculty of Geosciences and Environmental Studies, University of Mines and Technology, P. O. Box 237, Tarkwa, GhanaAs the global population is growing at a high rate, so is the electricity demand also increasing at a faster rate. This exerts pressure on electricity-generating plants and maintenance engineers because of the variability in demand. Avoiding disruption in the supply to meet demand requires forecasting what the future of demand will look like to be able to plan adequately towards it. This study, therefore, develops a new forecasting model using feature extraction (FE) where statistical information of the hourly demand data is extracted which serves as input variables for Backpropagation neural network (BPNN) optimized by particle swarm optimization (PSO) for electricity demand forecasting in Ghana. The model known as FE-PSO-BPNN is compared to other seven models such as Radial Basis Function (RBFNN), Random Forest (RF), Gradient Boosting Machine (GBM), Multivariate Adaptive Regression Splines (MARS), BPNN, and PSO-RBFNN where FE selects the input variables for all models. Electricity demand data from Ghana Grid Company from the period including 1st September 2018 to 30th November 2019 is used for the testing of the model's performance. Evaluation criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Scatter Index (SI) were used. The proposed model is more powerful in forecasting electricity demand than the others as it has RMSE (0.5344), MAE (3.3845), MAPE (0.1773), and SI (0.0003). The model is expected to be a better option for electricity sector managers when considering demand forecasting.http://www.sciencedirect.com/science/article/pii/S2772671123001882Electricity demand forecastingHybrid modelParticle swarm optimizationBackpropagation neural network |
| spellingShingle | Eric Ofori-Ntow Jnr Yao Yevenyo Ziggah Electricity demand forecasting based on feature extraction and optimized backpropagation neural network e-Prime: Advances in Electrical Engineering, Electronics and Energy Electricity demand forecasting Hybrid model Particle swarm optimization Backpropagation neural network |
| title | Electricity demand forecasting based on feature extraction and optimized backpropagation neural network |
| title_full | Electricity demand forecasting based on feature extraction and optimized backpropagation neural network |
| title_fullStr | Electricity demand forecasting based on feature extraction and optimized backpropagation neural network |
| title_full_unstemmed | Electricity demand forecasting based on feature extraction and optimized backpropagation neural network |
| title_short | Electricity demand forecasting based on feature extraction and optimized backpropagation neural network |
| title_sort | electricity demand forecasting based on feature extraction and optimized backpropagation neural network |
| topic | Electricity demand forecasting Hybrid model Particle swarm optimization Backpropagation neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2772671123001882 |
| work_keys_str_mv | AT ericoforintowjnr electricitydemandforecastingbasedonfeatureextractionandoptimizedbackpropagationneuralnetwork AT yaoyevenyoziggah electricitydemandforecastingbasedonfeatureextractionandoptimizedbackpropagationneuralnetwork |