Performance of exponential smoothing, a neural network and a hybrid algorithm to the short term load forecasting of batch and continuous loads

Mohammed, Jameel; Bahadoorsingh, S.; Ramsamooj, Neil; Sharma, Chandrabhan

doi:10.1109/ptc.2017.7980816

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…The determination of the number of hidden layers and hidden nodes in this study is following the rules, namely, the number of hidden nodes is 60% -90% of the number of input layers, the number of hidden nodes must not exceed 2 times the number of input nodes and the number of hidden layers must be between the number of input layers and the number. output layer (Mohammed et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…This research will combine two methods, namely ES and NN. Several other studies that are also of the same type include forecasting electrical loads where the combination of the two can produce better accuracy (Sulandari, Subanar, Suhartono, & Utami, 2016), (Mohammed, Bahadoorsingh, Ramsamooj, & Sharma, 2017), the application of NN to ES can reduce the error forecasting results by up to 6 percent (Parsi, 2016), TES-F provides better accuracy than forecasting with TES alone (Fajriyah et al, 2019), The combination of ES NN in predicting the number of broadband users in Indonesia can significantly improve accuracy than using only one method (Gunaryati et al, 2019), forecasting model produced by combining the ES-NN provides high accuracy (Smyl, 2020), and it turns out that the smoothing that has been done on the data can improve the performance of NN training (Muhamad & Din, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Smoothing in Neural Network for Univariat Time Series Data Forecasting

Syamsiah

Purwandani

2020

jri

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Time series data is interesting research material for many people. Not a few models have been produced, but very optimal accuracy has not been obtained. Neural network is one that is widely used because of its ability to understand non-linear relationships between data. This study will combine a neural network with exponential smoothing to produce higher accuracy. Exponential smoothing is one of the best linear methods is used for data set transformation and thereafter the new data set will be used in training and testing the Neural Network model. The resulting model will be evaluated using the standard error measure Root Mean Square Error (RMSE). Each model was compared with its RMSE value and then performed a T-Test. The proposed ES-NN model proved to have better predictive results than using only one method.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smoothing in Neural Network for Univariat Time Series Data Forecasting

Syamsiah

Purwandani

2020

jri

View full text Add to dashboard Cite

show abstract

“…After that, they did not consider how to apply those models in an optimized schedule, to avoid producing larger errors than past predictions that had already been calculated. J. Mohammed et al [26] did something similar, which also included reliability indicators to assess the model's performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Reduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting

et al. 2022

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Electrical energy is consumed at the same time as it is generated, since its storage is unfeasible. Therefore, short-term load forecasting is needed to manage energy operations. Due to better energy management, precise load forecasting indirectly saves money and CO2 emissions. In Europe, owing to directives and new technologies, prediction systems will be on a quarter-hour basis, which will reduce computation time and increase the computational burden. Therefore, a predictive system may not dispose of sufficient time to compute all future forecasts. Prediction systems perform calculations throughout the day, calculating the same forecasts repeatedly as the predicted time approaches. However, there are forecasts that are no more accurate than others that have already been made. If previous forecasts are used preferentially over these, then computational burden will be saved while accuracy increases. In this way, it will be possible to optimize the schedule of future quarter-hour systems and fulfill the execution time limits. This paper offers an algorithm to estimate which forecasts provide greater accuracy than previous ones, and then make a forecasting schedule. The algorithm has been applied to the forecasting system of the Spanish electricity operator, obtaining a calculation schedule that achieves better accuracy and involves less computational burden. This new algorithm could be applied to other forecasting systems in order to speed up computation times and to reduce forecasting errors.

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“…Data analytics, HPC, and cloud computing techniques are useful for dynamic energy management in smart grids. Mohammed et al evaluated the performance of a hybrid artificial neural network for short-term load forecasting [148]. Zhang et al built a demand response system for energy management with learning-based optimization technique [149].…”

Section: Decision-makingmentioning

confidence: 99%

Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review

et al. 2018

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With the rapid development of sensing, communication, computing technologies, and analytics techniques, today’s manufacturing is marching towards a new generation of sustainability, digitalization, and intelligence. Even though the significance of both sustainability and intelligence is well recognized by academia, industry, as well as governments, and substantial efforts are devoted to both areas, the intersection of the two has not been fully exploited. Conventionally, studies in sustainable manufacturing and smart manufacturing have different objectives and employ different tools. Nevertheless, in the design and implementation of smart factories, sustainability, and energy efficiency are supposed to be important goals. Moreover, big data based decision-making techniques that are developed and applied for smart manufacturing have great potential in promoting the sustainability of manufacturing. In this paper, the state-of-the-art of sustainable and smart manufacturing is first reviewed based on the PRISMA framework, with a focus on how they interact and benefit each other. Key problems in both fields are then identified and discussed. Specially, different technologies emerging in the 4th industrial revolution and their dedications on sustainability are discussed. In addition, the impacts of smart manufacturing technologies on sustainable energy industry are analyzed. Finally, opportunities and challenges in the intersection of the two are identified for future investigation. The scope examined in this paper will be interesting to researchers, engineers, business owners, and policymakers in the manufacturing community, and could serve as a fundamental guideline for future studies in these areas.

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Performance of exponential smoothing, a neural network and a hybrid algorithm to the short term load forecasting of batch and continuous loads

Cited by 12 publications

References 8 publications

Smoothing in Neural Network for Univariat Time Series Data Forecasting

Smoothing in Neural Network for Univariat Time Series Data Forecasting

Reduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting

Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review

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