A Literature Survey of Load Forecasting Methods and Impact of Different Factors on Load Forecasting

Kumar, Vikash

doi:10.22214/ijraset.2017.2067

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…These loads can be of different appliances heavily used in a different season. Temperature is an important factor in terms of the effect created in this method [28]. The random disturbance is the method in which load demand depends on the sudden disturbance like the suddenness of Television when Minister of any country goes public or come in any program.…”

Section: Related Workmentioning

confidence: 99%

Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine

et al. 2020

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Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier’s hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e., Genetic Algorithm (GA) and hyperparameters of SVM are tuned with the Grid Search Algorithm. The simulation results are shown in graphs and the values are shown in tabular form and they clearly show that our improved methods outperform State Of The Art (SOTA) methods in terms of accuracy and performance. The forecasting accuracy of Extreme Learning Machine based Genetic Algo (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS) is 96.3% and 93.25%, respectively. The accuracy of our improved techniques, i.e., ELM-GA and SVM-GS is 10% and 7%, respectively, higher than the SOTA techniques.

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Section: Related Workmentioning

confidence: 99%

Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine

et al. 2020

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“…In 2019, the average per capita volume of traffic usage reached 7.27GB per month [1] and it is expected to be 200 to 1,000 times in the following years [2]. Therefore, mobile operators face challenges in maintaining Quality of Service (QoS) by optimizing their network design planning as efficiently and quickly as possible [3][4][5][6][7][8]. Forecasting future traffic volume is crucial for successfully planning, managing, and developing network systems [9,10].…”

Section: Introductionmentioning

confidence: 99%

Forecasting LTE Network Traffic Using Hot-Winter’s Multiplicative Seasonal Method and Rolling Forecasting for Telecommunication Company Investment Optimization in Indonesia

2023

IJIES

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The rapid growth of mobile internet usage, particularly in LTE networks, poses challenges for mobile operators in maintaining quality of service (QoS) and optimizing network design planning. Accurate traffic volume forecasting is crucial for network planning and effective resource allocation. This study aims to propose an enhancement to the accuracy of the holt's winter multiplicative seasonal (HWMS) method by applying the rolling forecast technique to achieve better forecasting results. Using a public dataset of 56 cells, the evaluation based on mean absolute percentage error (MAPE) reveals the following sequence of prediction errors: HWMS & rolling forecast (20.47%), HWMS only (30.06%), FbProphet (30.45%), and auto regressive integrated moving average (ARIMA) (31.52%). The cells are categorized as "Good" (59%), "Reasonable" (39%), and "Poor" (2%). The results indicate that the proposed method achieves a significantly higher percentage of cells in the "Good" category, with a 45% difference compared to HWMS without rolling forecast, which only achieved 14%. Moreover, the proposed method outperforms ARIMA by 50% and FbProphet by 37% in the same category. Furthermore, when applied to real data from a telecommunications company in Indonesia, the proposed method identified 11 cells that require solutions out of a total of 100 cells. In comparison, the ARIMA method identified 3 cells, FbProphet identified 12 cells, and HWMS without Rolling Forecast identified 9 cells. Thus, the company can provide solutions for the identified 11 cells without the need for excessive investment while still maintaining revenue potential.

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A Literature Survey of Load Forecasting Methods and Impact of Different Factors on Load Forecasting

Cited by 2 publications

References 10 publications

Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine

Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine

Forecasting LTE Network Traffic Using Hot-Winter’s Multiplicative Seasonal Method and Rolling Forecasting for Telecommunication Company Investment Optimization in Indonesia

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