An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy

Yousaf, Adnan; Asif, Rameez; Shakir, Mustafa; Rehman, Ateeq Ur; Adrees, Mohmmed S.

doi:10.3390/su13116199

Cited by 37 publications

(12 citation statements)

References 55 publications

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“…Another training method is unsupervised training in which input is known but the output is unknown. The definition of the loss function and training schemes [55] are generally the two key pieces of information to provide in this type NN based scheme. The objective function or criterion is the function we wish to minimize or maximize.…”

Section: ) Neural Network Trainingmentioning

confidence: 99%

Training Strategy of Fuzzy-Firefly Based ANN in Non-Linear Channel Equalization

Mohapatra¹,

Rout²,

Bisoy

et al. 2022

IEEE Access

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Channel equalization is remaining a challenge for the researcher. Especially for the non-linear channel as well as the extremely dispersive channel, an effective channel equalizer is required. It is common knowledge that non-linear channel equalizers based on the neural networks (NN) outperform adaptive filter-based linear equalizers. To train NN equalizers, gradient-descent-based approaches like the back-propagation algorithm are often utilized, although they have drawbacks such as trapping of local minima, slower convergence, and compassion to log in. In this work, we presented a novel training strategy using a fuzzy firefly algorithm (FFA) for channel equalization. By using proper network topology and parameters, the suggested training system offers stronger exploitation and exploration skills, as well as the ability to solve the local minima issue. The performance of the equalizer can be analyzed by estimating two parameters i.e. MSE and BER. To exhibit the suggested technique's resilience in performance, the burst error situation was used, and the outcomes showed that the strategy is more effective in managing such situations than previous methods. The outcomes of the proposed method are presented through simulation, Furthermore, it proved that the suggested method validates a wide range of SNR, and also it outperforms the existing NN-based equalizers.

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Section: ) Neural Network Trainingmentioning

confidence: 99%

Training Strategy of Fuzzy-Firefly Based ANN in Non-Linear Channel Equalization

Mohapatra¹,

Rout²,

Bisoy

et al. 2022

IEEE Access

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“…One-year data of twelve months with the transformation of weekly data, ML, and feature selection are not considered as a final decision because the integration strategy looks toward the best algorithm of the related week of each month. Moreover, the MAPE calculations are modified as computed for the integration strategy in [46]. We computed the MAPE of PF for each month as follows.…”

Section: Proposed Integration Strategymentioning

confidence: 99%

A Novel Machine Learning-Based Price Forecasting for Energy Management Systems

et al. 2021

Self Cite

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Price forecasting (PF) is the primary concern in distributed power generation. This paper presents a novel and improved technique to forecast electricity prices. The data of various power producers, Capacity Purchase Price (CPP), Power Purchase Price (PPP), Tariff rates, and load demand from National Electric Power Regulatory Authority (NEPRA) are considered for MAPE reduction in PF. Eight time-series and auto-regression algorithms are developed for data fetching and setting the objective function. The feed-forward ANFIS based on the ML approach and space vector regression (SVR) is introduced to PF by taking the input from time series and auto-regression (AR) algorithms. Best-feature selection is conducted by adopting the Binary Genetic Algorithm (BGA)-Principal Component Analysis (PCA) approach that ultimately minimizes the complexity and computational time of the model. The proposed integration strategy computes the mean absolute percentage error (MAPE), and the overall improvement percentage is 9.24%, which is valuable in price forecasting of the energy management system (EMS). In the end, EMS based on the Firefly algorithm (FA) has been presented, and by implementing FA, the cost of electricity has been reduced by 21%, 19%, and 20% for building 1, 2, and 3, respectively.

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“…We used Black's model for option pricing [58][59][60][61]. Alternative approaches were explored by Yousaf, R. et al and Khan, R. et al in [62,63].…”

Section: Price Risk Mitigation Using Optionsmentioning

confidence: 99%

Using Artificial Neural Networks to Support the Decision-Making Process of Buying Call Options Considering Risk Appetite

2021

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During the COVID-19 pandemic, uncertainty has increased in many areas of both business supply and demand, notably oil demand and pricing have become even more unpredictable than before. Thus, for companies that buy large quantities of oil, effective oil price risk management is crucial for business success. Nevertheless, businesses’ risk appetite, specifically willingness to accept more risk to achieve desired business benefits, varies significantly. The aim of this paper is to deepen the analysis of the effectiveness of employing artificial neural networks (ANNs) in hedging against oil price changes by searching for buy signals for European WTI (West Texas Intermediate) crude oil call options, while taking into account the level of risk appetite. The number of generated buy signals decreases with increasing risk appetite, and thus the amount of capital necessary to buy options decreases. However, the results show that fewer buy signals do not necessarily translate into lower returns generated by networks in a given class. Thus, higher levels of return on the purchase of call options may be obtained. The conducted analyses clearly proved that ANNs can be a useful tool in the process of managing WTI crude oil price change risk. Using the analyzed network parameters, up to 29.9% of the theoretical maximum possible profit from buying options every day was obtained in the test set. Furthermore, all proposed networks generated some profit for the test set. The values of all indicators used in the analyses confirm that the ANNs can be effective regardless of the level of risk appetite, so in this respect they may be described as a universal decision support tool.

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An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy

Cited by 37 publications

References 55 publications

Training Strategy of Fuzzy-Firefly Based ANN in Non-Linear Channel Equalization

Training Strategy of Fuzzy-Firefly Based ANN in Non-Linear Channel Equalization

A Novel Machine Learning-Based Price Forecasting for Energy Management Systems

Using Artificial Neural Networks to Support the Decision-Making Process of Buying Call Options Considering Risk Appetite

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