Electricity Load and Price Forecasting Using Enhanced Machine Learning Techniques

Bano, Hamida; Tahir, Aroosa; Ali, Ishtiaq; Khan, Raja Jalees ul Hussen; Haseeb, Abdul; Javaid, Nadeem

doi:10.1007/978-3-030-22263-5_25

Cited by 6 publications

(14 citation statements)

References 19 publications

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“…The artificial neural network (ANN), support vector regression (SVR), and regression trees (RT) were used for the power output of photovoltaic (PV) systems [32]. For electricity load prediction, ehanced multilayer perceptron (MLP), enhanced support vector machine (SVM), and enhanced logistic regression (LR) were used [33]. Gradient boosted decision tree (GBDT), LR, random forest (RF), and MLP classifier were applied for smart grid stability prediction [34].…”

Section: Introductionmentioning

confidence: 99%

“…All these studies were not able to achieve effective results. Few studies utilized a large number of features [32,35,37] and employed a limited amount of data for modelling purposes [33,37]. Previous research did not work on class imbalance which caused insufficient results.…”

Section: Introductionmentioning

confidence: 99%

“…ML algorithms can detect trends and anomalies in datasets and thus help grid system operators to make real-time decisions for better distribution of available electricity [38]. Different approaches were used for the stability prediction of power grids, but effective results were not achieved [11,[31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources

Ibrar

Hassan

Shaukat

et al. 2022

Wireless Communications and Mobile Computing

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A decentralized power grid is a modern system that implements demand response without requiring major infrastructure changes. In decentralization, the consumers regulate their electricity demand autonomously based on the grid frequency. With cheap equipment (i.e., smart meters), the grid frequency can be easily measured anywhere. Electrical grids need to be stable to balance electricity supply and demand to ensure economically and dynamically viable grid operation. The volumes of electricity consumed/produced (p) by each grid participant, cost-sensitivity (g), and grid participants’ response times (tau) to changing grid conditions affect the stability of the grid. Renewable energy resources are volatile on varying time scales. Due to the volatile nature of these renewable energy resources, there are more frequent fluctuations in decentralized grids integrating renewable energy resources. The decentralized grid is designed by linking real-time electricity rates to the grid frequency over a few seconds to provide demand-side control. In this study, a model has been proposed to predict the stability of a decentralized power grid. The simulated data obtained from the online machine learning repository has been employed. Data normalization has been employed to reduce the biased behavior among attributes. Various data level resampling techniques have been used to address the issue of data imbalance. The results showed that a balanced dataset outperformed an imbalanced dataset regarding classifiers’ performance. It has also been observed that oversampling techniques proved better than undersampling techniques and imbalanced datasets. Overall, the XGBoost algorithm outperformed all other machine learning algorithms based on performance. XGBoost has been given an accuracy of 94.7%, but while combining XGBoost with random oversampling, its accuracy prediction has been improved to 96.8%. This model can better predict frequency fluctuations in decentralized power grids and the volatile nature of renewable energy resources resulting in better utilization. This prediction may contribute to the stability of a decentralized power grid for better distribution and management of electricity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources

Ibrar

Hassan

Shaukat

et al. 2022

Wireless Communications and Mobile Computing

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“…Abdulla et al [7] presented long-term forecasting of electrical loads in Kuwait using Prophet and Holt-Winters models of ten years based on the real data of historical electric load peaks (ten years). Hamida et al [14] also design enhanced machine learning techniques for electricity load and price forecasting, hourly data of one year is used for the forecasting process.…”

Section: Introductionmentioning

confidence: 99%

Scenarios Simulation and Analysis on Electric Power Planning Based on Multi-scale Forecast: A Case Study of Taoussa, Mali from 2020 to 2035

Kanté¹,

Li²,

Deng³

2021

Preprint

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A long-term forecast study on the electricity demand of Taoussa of Mali is conducted in this paper, with various scenarios of socioeconomic and technological conditions. The analysis tool, which is applied in scenarios simulation, is the Model for Analysis of Energy Demand from the International Atomic Energy Agency. The analysis results are annual electricity demand and peak load forecast for the electrification from the period 2020 to 2035. During the planning period, the analysis results show that the electricity demand will increase to 49.40 MW (332.57 GWh) for the low scenario (LS), 66.46 MW (472.61 GWh) for the reference scenario (RS), and 89.47 MW (635 GWh) for the high scenario (HS). In addition, the total electricity demand increased at an average rate of 8.13% in the LS, 10.31% in the RS and 12.56% in the HS in all sectors. The electricity peak demand is expected to grow at 7.92%, 10.53% and 12.91% corresponding to the three scenarios; in this case, the system peak demand in 2035 will increase to 64.88 MW for the LS, 92.2 MW for the RS and 126.22 MW, the days of peak load are between 17th -23rd in May. The Industry sector will be the biggest electricity consumer of Taoussa area.

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“…(Bano et al, 2019) anticipated cost and load of New York city by MLP, SVM and LR. The feature selection was done by CART and RFE (Khan et al, 2019).…”

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confidence: 99%

Forecasting IRAN’s max daily demand for electricity in different weather types using deep learning approach

Abdoli¹,

Mehrara²,

Ardalani³

2019

IJET

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Electricity network management is a complicated issue that needs a forecast of consumption. This predication is generally hard and a challenging topic due to nonlinearity and permanent fluctuation of load in the network. Although linearities of time-series load may give an estimation, linearization of nonlinear data to foresee removes the nature of information which shows anomalies in electricity demand. New approaches of (ANN) help to make a forecast model with keeping information properties. In this paper, we categorized max daily load data in IRAN based on climate zone to figure the following 2 months. Long Short-Term Memory (LSTM) from deep learning approach picks and yields contrasted with different steps of epochs. The outcomes appear epochs do not necessarily improve the accuracy of prediction and best MADP for cold, tropical and temperament zone, are 1.26%, 2.5%, and 10.49%, respectively.

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Electricity Load and Price Forecasting Using Enhanced Machine Learning Techniques

Cited by 6 publications

References 19 publications

A Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources

A Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources

Scenarios Simulation and Analysis on Electric Power Planning Based on Multi-scale Forecast: A Case Study of Taoussa, Mali from 2020 to 2035

Forecasting IRAN’s max daily demand for electricity in different weather types using deep learning approach

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