Forecasting hourly electricity demand using a hybrid method

Çevik, Hasan Hüseyin; Harmanci, Hüseyin; Cunkac, Mehmet

doi:10.1109/icced.2017.8019981

Cited by 6 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study results show that the hybridized SVM-ARIMA generates more reliable and realistic forecasts. Çevik et al, [19] in their study used ANN and Particle Swarm Optimization (PSO) techniques to forecast electricity load for 24 hours of next day. ANN weights of ANN were updated in the learning phase by PSO.…”

Section: Related Workmentioning

confidence: 99%

A Curve Fitting Modelling Approach to Forecast Long-Term Electrical Energy Consumption: Case Study of Turkey

Ayan

Şeker

Kasule

2021

Sakarya University Journal of Computer and Information Sciences

View full text Add to dashboard Cite

For Turkey to achieve the targets of Vision 2023 of being in the top ten economies of the world, the eleventh National Development Plan (NDP11) focuses on ensuring uninterrupted, high-quality, sustainable, reliable and affordable energy supply. In this regard medium-and long-term energy supplydemand planning is regarded as a key input to the planning process. Medium and long-term planning is possible only when reliable forecasts are available. Using Turkey's electrical energy consumption data from 1970 to 2015, this study presents novel Gaussian, Fourier and Exponential curve fitting and extrapolation approaches to forecast Turkey's electrical energy consumption up to the year 2025. Major interest is put on how the model forecasts electrical energy consumption for year 2023 because this year marks a century of the establishment of the Republic of Turkey and all strategic plans are focused on how to achieve the targets as outline in Vision 2023. We evaluate the performance of the models on how best they forecast electrical energy consumption for the year 2023. Our forecasts for the year 2023 are 352.7TWh, 377.4 TWh, and 460.1TWh for the Gaussian, Fourier and Exponential models respectively which compare well with NDP11's estimated 375.8 TWh electrical energy consumption in 2023.

show abstract

Section: Related Workmentioning

confidence: 99%

A Curve Fitting Modelling Approach to Forecast Long-Term Electrical Energy Consumption: Case Study of Turkey

Ayan

Şeker

Kasule

2021

Sakarya University Journal of Computer and Information Sciences

View full text Add to dashboard Cite

show abstract

“…PSO is a populationbased algorithm, that already resembles to GA in many aspects [16][17]. The iteration begins with a randomly determined particles which tries to reach the optimum result by providing particles to follow the best available solutions [18]. If PSO and GA are compared, PSO is easier to apply due to not containing crossing over and mutation operators.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Optimization of Location Assignment for Unit-Load AS/RS with a Dual-Shuttle

Çunkaş¹

2019

ijisae

View full text Add to dashboard Cite

Automated Storage and Retrieval Systems (AS/RS) offer several advantages such as gaining space, reducing human errors, and completing more storing and retrieving tasks in less time to large size warehouses. Machines included multi-shuttle systems are designed to carry multiple loads in one cycle on the purpose of develop the performance. This paper deals with a quadruple command cycle location assignment method in unit-load dual-shuttle AS/RS. Also, it is shown how to solve location assignment problem using Particle Swarm Optimization (PSO) with the objective of shortest travel distance therewithal shortest travel time. Binary Coded Genetic Algorithm (BGA) and Real Coded Genetic Algorithm (RGA) are developed to validate result obtained. At the end, ten numerical examples for two scenarios designed small size and large size are presented to prove the implementation of the proposed method. Results showed that PSO accomplished to find optimal solution for shortest travel distance of AS/RS machine.

show abstract

“…The results showed that the model could improve the accuracy of electricity demand prediction. A. Jain et al [25] proposed a short-term electricity demand prediction method based on seasonal double exponential smoothing. This method can accurately predict future electricity demand without the need for any external factors, while also handling complex seasonal trends.…”

mentioning

confidence: 99%

Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models

Yao,

Kao

2023

Applied Sciences

View full text Add to dashboard Cite

The energy discourse is multifaceted, encompassing energy creation, storage, and conservation. Beyond the imperative of conserving energy consumption, effective energy management is a critical aspect of achieving overall energy efficiency. Despite being traditionally regarded as low electricity consumers, retailers play a pivotal role in economic activity. While categorized as non-productive energy users, the retail industry operates numerous establishments, facing substantial energy costs that make energy management integral to its operations. Historically, smaller retail stores have lacked awareness of energy saving. However, by connecting these stores, even modest reductions in individual electricity consumption can yield significant overall energy savings. This study aims to investigate the feasibility of implementing the demand-side management (DSM) aggregator model in the retail industry. Through surveys on awareness of energy saving and the application of deep learning techniques to analyze the effectiveness of the Aggregator model, the results reveal that the mean squared prediction error (MSPE) of this research is below 2.05%. This indicates substantial accuracy and offers meaningful reference value for Energy Service Company (ESCO) providers. The findings contribute practical recommendations for the sustainable and competitive implementation of DSM energy management practices in smart cities.

show abstract

Forecasting hourly electricity demand using a hybrid method

Cited by 6 publications

References 21 publications

A Curve Fitting Modelling Approach to Forecast Long-Term Electrical Energy Consumption: Case Study of Turkey

A Curve Fitting Modelling Approach to Forecast Long-Term Electrical Energy Consumption: Case Study of Turkey

Optimization of Location Assignment for Unit-Load AS/RS with a Dual-Shuttle

Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models

Contact Info

Product

Resources

About