Data-driven random forest forecasting method of monthly electricity consumption

Pang, Xinfu; Luan, Changfeng; Liu, Li; Liu, Wei; Zhu, Yuchuan

doi:10.1007/s00202-021-01457-5

Cited by 9 publications

(11 citation statements)

References 26 publications

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“…• The machine learning techniques such as ANN, SVM, KNN, random forest, etc. were utilized for the prediction either individually or combined with clustering [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. These techniques already suffered from Mean Square Error (MSE), training error, and accuracy challenges.…”

Section: • Existing Clustering-based Methods [17] [18] [20] [24]mentioning

confidence: 99%

“…The instance was a house in Portugal with solar panels and batteries and a Home Energy Management System (HEMS) in charge. The largest reciprocal knowledge coefficient for monthly electricity use had proposed in [33]. First, the highest reciprocal knowledge coefficient had established between monthly power usage and its affecting elements.…”

Section: A State-of-artsmentioning

confidence: 99%

“…al [31], Bot et. al [32], and Pang et.al [33]. These methods are closely related to the proposed model where they have used Dragonfly and genetic algorithm in [30], Artificial bee optimization in [31], ensemble forecasting model in [32], and random forest model in [33] for energy consumption prediction.…”

Section: Woa-ann Predictive Modelmentioning

confidence: 99%

“…al [32], and Pang et.al [33]. These methods are closely related to the proposed model where they have used Dragonfly and genetic algorithm in [30], Artificial bee optimization in [31], ensemble forecasting model in [32], and random forest model in [33] for energy consumption prediction. Researchers have applied these methods using the dataset and other experimental settings mentioned above for the comparative study in this research article.…”

Section: Woa-ann Predictive Modelmentioning

confidence: 99%

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CAC-WOA: context aware clustering with whale optimization algorithm for knowledge discovery from multidimensional space in electricity application

Ahire

Patil

2023

Cluster Comput

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Due to the widespread use of smart metering infrastructure, multidimensional data on home electric consumption is easily available for studying its dynamics at finely resolved geographical and temporal scales. Effective forecasting and analysis of electric consumption are crucial for customer participation in time-of-use tariffs, critical peak pricing, and userspecific demand response programs derived from multidimensional data streams. Along with the enormous economic and sustainability ramifications, such as energy waste and the decarbonisation of the energy industry, precise consumption forecasts enable power system planning and reliable grid operations. Energy consumption forecasting is a hot field of research; despite the number of developed models, projecting electric consumption in residential buildings remains problematic owing to the significant unpredictability of occupant energy use behaviour. Discovering the electricity consumption knowledge from the Multi-Dimensional Data Streams (MDDS) of electricity logs is a challenging research problem. To end this, a novel electricity knowledge discovery model proposed from the MDDS using clustering and machine learning. Context-Aware Clustering with Whale Optimization Algorithm (CAC-WOA) is designed and explained in this research article. The CAC-WOA consists of two phases context-aware groups formation and WOA-based machine learning predictive model. In the CAC algorithm, group's formation using electricity contextual information to estimate the robust predictive features are proposed. Using such predictive features, the predictive model using the WOA-based Artificial Neural Network (ANN) is built. The modified ANN technique using the WOA algorithm is used to reduce the error rates and improve the prediction accuracy. The experimental outcomes using publically available electricity consumption datasets prove the efficiency of the CAC-WOA model.

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Section: • Existing Clustering-based Methods [17] [18] [20] [24]mentioning

confidence: 99%

Section: A State-of-artsmentioning

confidence: 99%

Section: Woa-ann Predictive Modelmentioning

confidence: 99%

Section: Woa-ann Predictive Modelmentioning

confidence: 99%

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CAC-WOA: context aware clustering with whale optimization algorithm for knowledge discovery from multidimensional space in electricity application

Ahire

Patil

2023

Cluster Comput

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“…A home in Portugal features a Home Energy Management System (HEMS), batteries, and solar panels. The value reported in [38] for the maximum mutual knowledge quantum for monthly power use. The elements with the highest reciprocal information coefficient have been selected.…”

Section: A Energy Forecasting Methodsmentioning

confidence: 99%

Context-Aware Clustering and the Optimized Whale Optimization Algorithm: An Effective Predictive Model for the Smart Grid

Ahire

Patil

2023

IJRITCC

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For customers to participate in key peak pricing, period-of-use fees, and individualized responsiveness to demand programmes taken from multi-dimensional data flows, energy use projection and analysis must be done well. However, it is a difficult study topic to ascertain the knowledge of use of electricity as recorded in the electricity records' Multi-Dimensional Data Streams (MDDS). Context-Aware Clustering (CAC) and the Optimized Whale Optimization Algorithm were suggested by researchers as a fresh power usage knowledge finding model from the multi-dimensional data streams (MDDS) to resolve issue (OWOA). The proposed CAC-OWOA framework first performs the data cleaning to handle the noisy and null elements. The predictive features are extracted from the novel context-aware group formation algorithm using the statistical context parameters from the pre-processed MDDS electricity logs. To perform the energy consumption prediction, researchers have proposed the novel Artificial Neural Network (ANN) predictive algorithm using the bio-inspired optimization algorithm called OWOA. The OWOA is the modified algorithm of the existing WOA to overcome the problems of slow convergence speed and easily falling into the local optimal solutions. The ANN training method is used in conjunction with the suggested bio-inspired OWOA algorithm to lower error rates and boost overall prediction accuracy. The efficiency of the CAC-OWOA framework is evaluated using the publicly available smart grid electricity consumption logs. The experimental results demonstrate the effectiveness of the CAC-OWOA framework in terms of forecasting accuracy, precision, recall, and duration when compared to underlying approaches.

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Global horizontal irradiance prediction for renewable energy system in Najran and Riyadh

Irfan,

Shaf,

Ali

et al. 2024

AIP Advances

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Producing and supplying energy efficiently are important for many countries. Using models to predict energy production can help reduce costs, improve efficiency, and make energy systems work better. This research predicts solar electricity production in the Najran and Riyadh regions of Saudi Arabia by analyzing 14 weather factors. The weather factors that were considered in the study include date, time, Global Horizontal Irradiance (GHI), clear sky, top of atmosphere, code, temperature, relative humidity, pressure, wind speed, wind direction, rainfall, snowfall, and snow depth. GHI is the most important factor because it determines how much solar energy a system can produce. Therefore, it is important to be able to predict GHI accurately. This study used a variety of data-driven models to predict GHI, including the elastic net regression, linear regression, random forest, k-nearest neighbor, gradient boosting regressor, light gradient boosting regressor, extreme gradient boosting regressor, and decision tree regressor. The models were evaluated using a set of metrics, including the mean absolute error, mean squared error, root mean square error, coefficient of determination (R2), and adjusted coefficient of determination. This study found that the decision tree regression, Random Forest (RF), and Extreme Gradient Boosting (XGB) models performed better in the Riyadh region than in the Najran region. The R2 values for the Riyadh region were 99%, 99%, and 98%, while the R2 values for the Najran region were 89%, 94%, and 94%. This suggests that the Riyadh region is a more suitable location for solar energy conversion systems. These findings are important for policymakers and investors who are considering the development of solar energy projects in Saudi Arabia.

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Data-driven random forest forecasting method of monthly electricity consumption

Cited by 9 publications

References 26 publications

CAC-WOA: context aware clustering with whale optimization algorithm for knowledge discovery from multidimensional space in electricity application

CAC-WOA: context aware clustering with whale optimization algorithm for knowledge discovery from multidimensional space in electricity application

Context-Aware Clustering and the Optimized Whale Optimization Algorithm: An Effective Predictive Model for the Smart Grid

Global horizontal irradiance prediction for renewable energy system in Najran and Riyadh

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