Short-Term Electricity Demand Forecasting Using Deep Neural Networks: An Analysis for Thai Data Article Swipe
Kamal Chapagain
,
Samundra Gurung
,
Pisut Kulthanavit
,
Somsak Kittipiyakul
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/asi6060100
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/asi6060100
Electricity demand forecasting plays a significant role in energy markets. Accurate prediction of electricity demand is the key factor in optimizing power generation and consumption, saving energy resources, and determining energy prices. However, integrating energy mix scenarios, including solar and wind power, which are highly nonlinear and seasonal, into an existing grid increases the uncertainty of generation, creating additional challenges for precise forecasting. To tackle such challenges, state-of-the-art methods and algorithms have been implemented in the literature. Artificial Intelligence (AI)-based deep learning models can effectively handle the information of long time-series data. Based on patterns identified in datasets, various scenarios can be developed. In this paper, several models were constructed and tested using deep AI networks in two different scenarios: Scenario1 used data for weekdays, excluding holidays, while Scenario2 used the data without exclusion. To find the optimal configuration, the models were trained and tested within a large space of alternative hyperparameters. We used an Artificial Neural Network (ANN)-based Feedforward Neural Network (FNN) to show the minimum prediction error for Scenario1 and a Recurrent Neural Network (RNN)-based Gated Recurrent Network (GRU) to show the minimum prediction error for Scenario2. From our results, it can be concluded that the weekday dataset in Scenario1 prepared by excluding weekends and holidays provides better forecasting accuracy compared to the holistic dataset approach used in Scenario2. However, Scenario2 is necessary for predicting the demand on weekends and holidays.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/asi6060100
- https://www.mdpi.com/2571-5577/6/6/100/pdf?version=1698402173
- OA Status
- gold
- Cited By
- 5
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387976194
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387976194Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/asi6060100Digital Object Identifier
- Title
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Short-Term Electricity Demand Forecasting Using Deep Neural Networks: An Analysis for Thai DataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-27Full publication date if available
- Authors
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Kamal Chapagain, Samundra Gurung, Pisut Kulthanavit, Somsak KittipiyakulList of authors in order
- Landing page
-
https://doi.org/10.3390/asi6060100Publisher landing page
- PDF URL
-
https://www.mdpi.com/2571-5577/6/6/100/pdf?version=1698402173Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2571-5577/6/6/100/pdf?version=1698402173Direct OA link when available
- Concepts
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Computer science, Artificial neural network, Demand forecasting, Artificial intelligence, Recurrent neural network, Hyperparameter, Machine learning, Time series, Feedforward neural network, Term (time), Deep learning, Data mining, Operations research, Engineering, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
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55Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.is_published | True |
| primary_location.raw_source_name | Applied System Innovation |
| primary_location.landing_page_url | https://doi.org/10.3390/asi6060100 |
| publication_date | 2023-10-27 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2275088575, https://openalex.org/W1720804347, https://openalex.org/W2794778778, https://openalex.org/W4307940504, https://openalex.org/W3185376699, https://openalex.org/W2608573139, https://openalex.org/W6767885493, https://openalex.org/W2794794675, https://openalex.org/W2202089267, https://openalex.org/W2747580724, https://openalex.org/W2145732764, https://openalex.org/W6603182422, https://openalex.org/W2970658101, https://openalex.org/W2064650039, https://openalex.org/W2593505840, https://openalex.org/W2944673875, https://openalex.org/W1995351367, https://openalex.org/W2768050941, https://openalex.org/W2076355256, https://openalex.org/W2999869395, https://openalex.org/W2809317444, https://openalex.org/W3024619383, https://openalex.org/W3205100669, https://openalex.org/W4363673970, https://openalex.org/W4363676341, https://openalex.org/W2151767444, https://openalex.org/W4309603545, https://openalex.org/W2343283104, https://openalex.org/W2912281968, https://openalex.org/W4387635252, https://openalex.org/W2906108515, https://openalex.org/W2365655928, https://openalex.org/W2597866042, https://openalex.org/W2054978208, https://openalex.org/W1990440379, https://openalex.org/W2906596999, https://openalex.org/W2169176023, https://openalex.org/W2970960007, https://openalex.org/W2948490758, https://openalex.org/W6781956733, https://openalex.org/W2166846656, https://openalex.org/W2945039973, https://openalex.org/W2010069097, https://openalex.org/W2059728786, https://openalex.org/W1986511531, https://openalex.org/W3213532607, https://openalex.org/W4293125186, https://openalex.org/W4295528346, https://openalex.org/W4297348473, https://openalex.org/W2090322886, https://openalex.org/W1995027390, https://openalex.org/W2561043568, https://openalex.org/W79427069, https://openalex.org/W3082175554, https://openalex.org/W2971724044 |
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| corresponding_author_ids | https://openalex.org/A5037958608, https://openalex.org/A5085579454 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I13590829, https://openalex.org/I47881588 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8899999856948853 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.72646425 |
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| citation_normalized_percentile.is_in_top_10_percent | False |