Relevant bibliographies by topics / Electricity load forecasting

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Electricity load forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Electricity load forecasting.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Electricity load forecasting"

1

Nor, Maria Elena Binti, Mohd Saifullah Rusiman, Suliadi Firdaus Sufahani, Mohd Asrul Affendi Abdullah, Sathwinee A/P Bataraja, and Sabariah Saharan. "Deseasonalisation in Electricity Load Forecasting." International Journal of Engineering & Technology 7, no. 4.30 (2018): 448. http://dx.doi.org/10.14419/ijet.v7i4.30.22364.

Full text
Abstract:
Nowadays, there is an increasing demand for electricity however overproduction of electricity lead to wastage. Therefore, electricity load forecasting plays a crucial role in operation, planning and maintenance of power system. This study was designed to investigate the effect of deseasonalisation on electricity load data forecasting. The daily seasonality in electricity load data was removed and the forecast methods were employed on both the seasonal data and non-seasonal data. Holt Winters method and Seasonal-Autoregressive Integrated Moving Average (SARIMA) methods were used on the seasonal
APA, Harvard, Vancouver, ISO, and other styles
2

Liu, Yali, Tingting Chai, Zhaoxin Zhang, and Gang Long. "Towards Electricity Price and Electric Load Forecasting Using Multi-task Deep Learning." Journal of Physics: Conference Series 2171, no. 1 (2022): 012048. http://dx.doi.org/10.1088/1742-6596/2171/1/012048.

Full text
Abstract:
Abstract The continuous development of the power Internet of Things (IOT) has enabled power market participants to obtain a large amount of data. Simultaneously, the power IOT has an increasing demand for power load and electricity price forecasting; Since the forecasting of electricity load and electricity price is a single task, and the model calculation accuracy is not high, this brings great challenges to the accurate forecasting of electricity load and electricity price. In this paper, two power load and electricity price forecasting models via multi-task deep learning are established per
APA, Harvard, Vancouver, ISO, and other styles
3

Li, Jie, Yiming Wu, Yong He, and Hang Wei. "Construction of short-term electricity demand streaming forecasting model in demand-side dynamic response." Journal of Physics: Conference Series 2728, no. 1 (2024): 012076. http://dx.doi.org/10.1088/1742-6596/2728/1/012076.

Full text
Abstract:
Abstract In the context of demand-side dynamic response, the electricity supply-demand relationship undergoes constant changes, and short-term electricity load exhibits strong randomness and volatility, making load conditions challenging to predict. Hence, this paper proposes a short-term electricity demand streaming forecasting model that combines wavelet decomposition with Random Forest to enhance the accuracy of short-term electricity load forecasting. This model establishes a load feature system, utilizing a three-scale wavelet decomposition algorithm to break down the load sequence into s
APA, Harvard, Vancouver, ISO, and other styles
4

Rizaldi, Amri M., Ahmad Ridwan, Yuan Anisa, et al. "Short-Term Forecasting of Electricity Consumption Using Fuzzy Logic." Journal of Renewable Energy, Electrical, and Computer Engineering 3, no. 2 (2023): 44. http://dx.doi.org/10.29103/jreece.v3i2.11281.

Full text
Abstract:
The high demand for electricity in the production process at PT Semen Padang requires a system that can cope with various kinds of disturbances. The problem is that the need for electrical loads is dynamic, especially in the short term, allowing fluctuations between electrical loads at uncertain times. A short-term electric energy consumption forecasting method is needed to determine load growth and distributed power supply. This research aims to use a fuzzy logic algorithm to perform short-term electrical energy consumption forecasting and compare the forecasting results with the actual load
APA, Harvard, Vancouver, ISO, and other styles
5

Sičaja, Ivona, Ante Previšić, Matija Zečević, and Domagoj Budiša. "Evaluation of load forecast model performance in Croatian DSO." Journal of Energy - Energija 67, no. 2 (2022): 54–62. http://dx.doi.org/10.37798/201867280.

Full text
Abstract:
During the revitalization of the Remote Control Systems of four Distribution System Operators in Croatia: Elektra Zagreb, Elektroslavonija Osijek, Elektroprimorje Rijeka and Elektrodalmacija Split, the load forecasting subsystems were implemented as an integral part of the DMS system. Accurate electricity load forecasting presents an important challenge in managing supply and demand of electricity since it cannot be stored and has to be consumed immediately. Electricity consumption forecasting has an important role in the scheduling, capacity and operational planning of the distribution power
APA, Harvard, Vancouver, ISO, and other styles
6

Susatyo Handoko and Karnoto Karnoto. "Daily Peak Load Forecasting At PT. PLN Uses Anfis(Adaptive Neuro-Fuzzy Inference System)." Journal of Scientific Interdisciplinary 2, no. 3 (2025): 1–8. https://doi.org/10.62504/jis1250.

Full text
Abstract:
The demand for electrical energy continues to rise with the progression of time. This growth must be matched by a reliable and cost-effective supply of electricity, requiring power systems that are both dependable and economical. Since the amount of electricity consumed by users cannot be precisely predicted, balancing generation with consumption necessitates accurate electrical load forecasting. This study focuses on load forecasting using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The forecast developed targets daily peak loads, which fall under short-term load forecasting. Th
APA, Harvard, Vancouver, ISO, and other styles
7

Anju, S. Jyothis Joseph &. Ojus Thomas Lee. "ELECTRICITY LOAD FORECASTING USING STATISTICAL METHODS - A STUDY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY AICT 2019 (April 4, 2019): 18–28. https://doi.org/10.5281/zenodo.2629233.

Full text
Abstract:
Load forecasting is the estimation of electrical power required to meet the long, medium   or short term demand by the construction of models based on relative information, such   as historic load data, climate etc. It helps the electric power generation and distribution systems to plan and optimize their operations. It includes energy purchase and generation, infrastructure development, load switching, and contract evaluation. Accurately forecasting the load enables the utility companies to gain profit. Different techniques have been developed for forecasting load, which i
APA, Harvard, Vancouver, ISO, and other styles
8

Tang, Ruoli, Siwen Ning, Zongyang Ren, Xin Li, and Yan Zhang. "Novel Load Forecasting and Optimal Dispatching Methods Considering Demand Response for Integrated Port Energy System." Journal of Marine Science and Engineering 13, no. 3 (2025): 421. https://doi.org/10.3390/jmse13030421.

Full text
Abstract:
The optimal dispatching of integrated energy systems can effectively reduce energy costs and decrease carbon emissions. The accuracy of the load forecasting method directly determines the dispatching outcomes, yet considering the stochastic and non-periodic characteristics of port electricity load, traditional load forecasting methods may not be suitable due to the weak historical regularity of the load data themselves. Therefore, this paper proposes a method for forecasting the electricity load of container ports based on ship arrival and departure schedules as well as port handling tasks. By
APA, Harvard, Vancouver, ISO, and other styles
9

Khan, Salahuddin. "Short-Term Electricity Load Forecasting Using a New Intelligence-Based Application." Sustainability 15, no. 16 (2023): 12311. http://dx.doi.org/10.3390/su151612311.

Full text
Abstract:
Electrical load forecasting plays a crucial role in planning and operating power plants for utility factories, as well as for policymakers seeking to devise reliable and efficient energy infrastructure. Load forecasting can be categorized into three types: long-term, mid-term, and short-term. Various models, including artificial intelligence and conventional and mixed models, can be used for short-term load forecasting. Electricity load forecasting is particularly important in countries with restructured electricity markets. The accuracy of short-term load forecasting is crucial for the effici
APA, Harvard, Vancouver, ISO, and other styles
10

Madhiarasan, Manogaran, and Mohamed Louzazni. "Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network." Forecasting 3, no. 4 (2021): 804–38. http://dx.doi.org/10.3390/forecast3040049.

Full text
Abstract:
With an uninterrupted power supply to the consumer, it is obligatory to balance the electricity generated by the electricity load. The effective planning of economic dispatch, reserve requirements, and quality power provision for accurate consumer information concerning the electricity load is needed. The burden on the power system engineers eased electricity load forecasting is essential to ensure the enhanced power system operation and planning for reliable power provision. Fickle nature, atmospheric parameters influence makes electricity load forecasting a very complex and challenging task.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Electricity load forecasting"

1

SOBRAL, ANA PAULA BARBOSA. "FORECASTING HOURLY ELECTRICITY LOAD FOR LIGHT." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1999. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7464@1.

Full text
Abstract:
Nessa dissertação é desenvolvido um modelo de previsão de curto prazo para cargas horárias empregando informações climáticas. Tal modelo é montado para a companhia de eletricidade LIGHT. O modelo proposto combina diferentes metodologias, são elas: Redes Neurais, Métodos Estatísticos e Lógica Nebulosa. Primeiramente, emprega-se o Mapa Auto-Organizável de Kohonen para identificar as curvas típicas de carga que são incluídas em um modelo de previsão estatística. Com intuito de melhorar o desempenho do modelo em termos do erro de previsão é adicionado, através de Lógica Nebulosa, o efei
APA, Harvard, Vancouver, ISO, and other styles
2

Alam, Samiul. "Recurrent neural networks in electricity load forecasting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233254.

Full text
Abstract:
In this thesis two main studies are conducted to compare the predictive capabilities of feed-forward neural networks (FFNN) and long short-term memory networks (LSTM) in electricity load forecasting. The first study compares univariate networks using past electricity load, as well as multivariate networks using past electricity load and air temperature, in day-ahead load forecasting using varying lookback periods and sparsity of past observations. The second study compares FFNNs and LSTMs of different complexities (i.e. network sizes) when restrictions imposed by limitations of the real world
APA, Harvard, Vancouver, ISO, and other styles
3

Kardehi, Moghaddam Mehdi. "Introducing system-based spatial electricity load forecasting." Thesis, Kardehi Moghaddam, Mehdi (2016) Introducing system-based spatial electricity load forecasting. PhD thesis, Murdoch University, 2016. https://researchrepository.murdoch.edu.au/id/eprint/33975/.

Full text
Abstract:
The main motivation of this research is to help reduce the Green House Gases (GHG) emissions of the electricity sector, and counteract the effects on nature and people. Traditional methods of power planning are not optimised to achieve this, and only consider Capital Expenditure (Capex) and Operational Expenditure (Opex) reduction as their main objectives. Minimising GHG emissions is now an additional objective of power planning. One way of achieving this is by optimising the distance of generators to the loads to reduce the transmission losses, and also by harnessing the available regional so
APA, Harvard, Vancouver, ISO, and other styles
4

Shepero, Mahmoud. "Modeling and forecasting the load in the future electricity grid : Spatial electric vehicle load modeling and residential load forecasting." Licentiate thesis, Uppsala universitet, Fasta tillståndets fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-359432.

Full text
Abstract:
The energy system is being transitioned to increase sustainability. This transition has been accelerated by the increased awareness about the adverse effects of the greenhouse gas (GHG) emissions into the atmosphere. The transition includes switching to electricity as the energy carrier in some sectors, e.g., transportation, increasing the contribution of renewable energy sources (RES) to the grid, and digitalizing the grid services. Electric vehicles (EVs) are promoted and subsidized in many countries among the sustainability initiatives. Consequently, the global sales of EVs rapidly increase
APA, Harvard, Vancouver, ISO, and other styles
5

Zhao, Lu. "Machine Learning in Electricity Load Forecasting of Prosumer Buildings." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298022.

Full text
Abstract:
Short-term load forecasting plays a key role in energy optimizations such as peaking shaving and cost arbitrage. Forecasting the aggregated load of a city or region has been researched for years and produced accurate results with time lead ranging from an hour to a week. However, little attention has been paid to the building level due to the fact that its dynamics are considerably different from those of a utility or other middle or large-scale customers. This thesis work focuses on short-term load forecasting at a building level, which is more challenging and will be taken as the pre-work fo
APA, Harvard, Vancouver, ISO, and other styles
6

SILVA, HELIO FRANCISCO DA. "ON ADDRESSING IRREGULARITIES IN ELECTRICITY LOAD TIME-SERIES AND SHORT TERM LOAD FORECASTING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2001. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=1737@1.

Full text
Abstract:
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>As alterações na legislação do Setor de Energia Elétrica Brasileiro em fins do milênio passado, provocou profundas mudanças no planejamento da Operação do Sistema e na Comercialização de energia elétrica no Brasil. O desmembramento das atividades de geração, de transmissão e de distribuição de energia elétrica criou novas características no comportamento dos Agentes Concessionários e as previsões de demanda por energia elétrica, que sempre foram ferramenta importante, por exemplo, na programação da operação, passaram a ser
APA, Harvard, Vancouver, ISO, and other styles
7

Berk, Kevin [Verfasser]. "Probabilistic Forecasting of Electricity Load for Industrial Enterprises / Kevin Berk." München : Verlag Dr. Hut, 2017. http://d-nb.info/1135594392/34.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Al-Aoudah, Ahmed A. "Long term load forecasting for the Central Region of Saudi Arabia." Thesis, Cranfield University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250633.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

FARIAS, DOUGLAS ALEXANDER ALVES DE. "DAILY ELECTRICITY FORECASTING IN LOAD LEVELS, COMBINING STATISTICAL AND COMPUTATIONAL INTELLIGENCE TOOLS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13211@1.

Full text
Abstract:
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>OPERADOR NACIONAL DO SISTEMA ELÉTRICO<br>Esta dissertação apresenta um estudo sobre o comportamento da carga de energia agregada em intervalos temporais dentro de um mesmo dia. Esse tipo de agregação já vem sendo utilizado no setor elétrico brasileiro, sob a forma de três patamares de carga, denominados leve, média e pesada. No entanto, tais patamares são sempre obtidos indiretamente, a partir da agregação da carga horária, não tendo sido encontrado, até a publicação dessa dissertação, nenhum tratamento de forma direta dos mesmos. O t
APA, Harvard, Vancouver, ISO, and other styles
10

Oscar, Nordström. "Multivariate Short-term Electricity Load Forecasting with Deep Learning and exogenous covariates." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-183982.

Full text
Abstract:
Maintaining the electricity balance between supply and demand is a challenge for electricity suppliers. If there is an under or overproduction, it entails financial costs and affects consumers and the climate. To better understand how to maintain the balance, can the suppliers use short-term forecasts of electricity load. Hence it is of paramount importance that the forecasts are reliable and of high accuracy. Studies show that time series modeling moves towards more data-driven methods, such as Artificial Neural Networks due to their ability to extract complex relationships and flexibility. T
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Electricity load forecasting"

1

Institute, Edison Electric, ed. A Guide to electricity forecasting methodology. Edison Electric Institute, 1986.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Antoniadis, Anestis, Jairo Cugliari, Matteo Fasiolo, Yannig Goude, and Jean-Michel Poggi. Statistical Learning Tools for Electricity Load Forecasting. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-60339-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Inc, Regional Economic Research, Barakat & Chamberlin., and Electric Power Research Institute, eds. Drivers of electricity growth and the role of utility demand-side management. Electric Power Research Institute, 1993.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Pillon, Jeffrey R. The Michigan Public Service Commission staff forecast for Consumers Power Company's electricity sales and peak load, 1993 to 2007. Forecasting Section, Strategic Planning Division, Public Service Commission, Michigan Dept. of Commerce, 1993.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

R, Pillon Jeffrey, Michigan Public Service Commission, and Michigan. Public Service Commission. Office of Planning, Policy, and Evaluation. Forecasting Section., eds. The Michigan Public Service Commission staff forecast for Consumers Power Company's electricity sales and peak load, 1990 to 2005. The Commission, 1991.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Pillon, Jeffrey R. The Michigan Public Service Commission staff forecast for the Detroit Edison Company's electricity sales and peak load, 1991 to 2006. Forecasting Section, Strategic Planning Division, Public Service Commission, Michigan Dept of Commerce, 1993.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Weron, Rafał. Modeling and Forecasting Electricity Loads and Prices. John Wiley & Sons Ltd, 2006. http://dx.doi.org/10.1002/9781118673362.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Jamasb, Tooraj. The future of electricity demand: Customers, citizens, and loads. Cambridge University Press, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

G, Pollitt Michael, ed. The future of electricity demand: Customers, citizens, and loads. Cambridge University Press, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Cugliari, Jairo, Matteo Fasiolo, and Yannig Goude. Statistical Learning Tools for Electricity Load Forecasting. Springer International Publishing AG, 2024.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Electricity load forecasting"

1

Jacob, Maria, Cláudia Neves, and Danica Vukadinović Greetham. "Short Term Load Forecasting." In Forecasting and Assessing Risk of Individual Electricity Peaks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28669-9_2.

Full text
Abstract:
Abstract Electrification of transport and heating, and the integration of low carbon technologies (LCT) is driving the need to know when and how much electricity is being consumed and generated by consumers. It is also important to know what external factors influence individual electricity demand.
APA, Harvard, Vancouver, ISO, and other styles
2

Hyde, O., and P. F. Hodnett. "An Electricity Load Forecasting System." In Proceedings of the Fifth European Conference on Mathematics in Industry. Vieweg+Teubner Verlag, 1991. http://dx.doi.org/10.1007/978-3-663-01312-9_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Himych, Omar, Amaury Durand, and Yannig Goude. "Adaptive Forecasting of Extreme Electricity Load." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62700-2_19.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Haben, Stephen, Marcus Voss, and William Holderbaum. "Primer on Distribution Electricity Networks." In Core Concepts and Methods in Load Forecasting. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27852-5_2.

Full text
Abstract:
AbstractThis chapter gives a brief overview of the electricity distribution network. This knowledge is important to understand some of the core features of the network and the corresponding data, what are the main of applications, and how to create an appropriate forecast model.
APA, Harvard, Vancouver, ISO, and other styles
5

Berk, Kevin. "A one factor model for medium-term load forecasting." In Modeling and Forecasting Electricity Demand. Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-08669-5_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Koprinska, Irena, Mashud Rana, and Vassilios G. Agelidis. "Electricity Load Forecasting: A Weekday-Based Approach." In Artificial Neural Networks and Machine Learning – ICANN 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33266-1_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Hyde, O., and P. F. Hodnett. "Implementation of an Electricity Load Forecasting System." In European Consortium for Mathematics in Industry. Vieweg+Teubner Verlag, 1992. http://dx.doi.org/10.1007/978-3-663-09834-8_37.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Viegas, Joaquim L., Susana M. Vieira, Rui Melício, Victor M. F. Mendes, and João M. C. Sousa. "GA-ANN Short-Term Electricity Load Forecasting." In Technological Innovation for Cyber-Physical Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31165-4_45.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Martín-Merino, Manuel, and Jesus Román. "Electricity Load Forecasting Using Self Organizing Maps." In Artificial Neural Networks – ICANN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840930_74.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Antoniadis, Anestis, Jairo Cugliari, Matteo Fasiolo, Yannig Goude, and Jean-Michel Poggi. "Mixed Effects Models for Electricity Load Forecasting." In Statistics for Industry, Technology, and Engineering. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-60339-6_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Electricity load forecasting"

1

Wang, Xiaochen, Fan Bai, and Yuntong Cai. "Electricity load forecasting using large language models." In International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2024), edited by Liang Hu. SPIE, 2025. https://doi.org/10.1117/12.3064969.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Long, Lingli, Yi Yu, Yongjin Zhu, Kunming Li, Zheng Kong, and Benchang Ma. "Medium-Term Load Forecasting Based on Electricity Constraint." In 2024 14th International Conference on Power and Energy Systems (ICPES). IEEE, 2024. https://doi.org/10.1109/icpes63746.2024.10856579.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Li, Xiwang, and Senghe Cui. "An Enhanced iTransformer-Based Model for Electricity Load Forecasting." In 2024 10th International Conference on Computer and Communications (ICCC). IEEE, 2024. https://doi.org/10.1109/iccc62609.2024.10942128.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Lu, Rui, Yankun Feng, Minyi Zhuo, and Qiang Li. "Load Forecasting for Classified Agency Electricity Purchasing Customers Based on Electricity Usage Characteristics Analysis." In 2025 7th Asia Energy and Electrical Engineering Symposium (AEEES). IEEE, 2025. https://doi.org/10.1109/aeees64634.2025.11020515.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Xing, Jiwei, Xinying Xu, Chao Liu, and Dongmei Hu. "Electricity Load Forecasting Based on Patch Channel-Mixing Transformer Model." In 2024 6th International Conference on Electronic Engineering and Informatics (EEI). IEEE, 2024. http://dx.doi.org/10.1109/eei63073.2024.10696901.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Hussain, Adil, Vineet Dhanawat, Ayesha Aslam, Tanq, and Faizan Zaman. "Electricity Load Forecasting Using Attention-Based Hybrid Deep Learning Model." In 2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET). IEEE, 2024. http://dx.doi.org/10.1109/iicaiet62352.2024.10729950.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Le, Tuan Minh, Huy Gia Tran, Long TonThat, Tan Duc Tran, Anh Trung Do, and Son Vu Truong Dao. "Application of Machine Learning in Forecasting Short-Term Electricity Load." In 2024 International Conference on Advanced Technologies for Communications (ATC). IEEE, 2024. https://doi.org/10.1109/atc63255.2024.10908129.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Pliansaad, Kanya, Duangjai Jitkongchuen, and Panita Thusaranon. "Forecasting Customer Baseline Load Based on Similar Electricity Usage Patterns." In 2025 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON). IEEE, 2025. https://doi.org/10.1109/ectidamtncon64748.2025.10962073.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Ning, Yongjie, Junyi Wang, Zhiying Wang, Ying Zhu, and Jing Zhang. "Electricity Load Forecasting Method Based on Improved PSO-LSTM Modeling." In 2025 International Conference on Power Electronics and Electric Drives (PEED). IEEE, 2025. https://doi.org/10.1109/peed63748.2025.00019.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Pisaneschi, Giulio, Davide Fioriti, Sandra Banda, Anne Wacera Wambugu, Izael Da Silva, and Davide Poli. "Electricity Forecasting in Kenyan Off-grid Microgrid: Forecasting Accuracy Versus Multi-Year Load Growth." In 2024 IEEE International Humanitarian Technologies Conference (IHTC). IEEE, 2024. https://doi.org/10.1109/ihtc61819.2024.10855058.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Electricity load forecasting"

1

Cox, Jordan, Thushara De Silva, Jennie Jorgenson, and Barbara O'Neill. Load Forecasting for the Moroccan Electricity Sector. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1818879.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zhou, Ella, Sika Gadzanku, Cabell Hodge, Mike Campton, Stephane de la Rue du Can, and Jingjing Zhang. Best Practices in Electricity Load Modeling and Forecasting for Long-Term Power System Planning. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1972011.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Zhou, Ella, Sika Gadzanku, Cabell Hodge, Mike Campton, and Jingjing Zhang. Best Practices in Electricity Load Modeling and Forecasting for Long-Term Power System Planning. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1973409.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Electricity load forecasting' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography