Long-term energy peak load forecasting models: A hybrid statistical approach

Khorsheed, Eman

doi:10.1109/icaset.2018.8376792

Cited by 9 publications

(10 citation statements)

References 14 publications

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“…This research extends the work of Khorsheed (2018). Here a new “hybrid” method is developed based on the weighted sum of exponential smoothing forecasts of Holt-Winters (HW) and a set of forecasts obtained by implementing Markov chain Monte Carlo (MCMC) techniques to a Bayesian regression model.…”

Section: Introductionmentioning

confidence: 85%

“…Based on the results of the technique developed by Khorsheed (2018), three load-affecting factors are considered in the present study of which two correspond to calendar (month and year) and one to weather effects (maximum air temperature). The technique requires proposing a probabilistic model for peak load responses, Y of the form: where f ( β, X ) is some deterministic function of regression coefficients β and input vectors X of load affecting factors and e is a zero-mean additive Gaussian noise vector.…”

Section: Bayesian Forecastingmentioning

confidence: 99%

“…Khorsheed (2018) developed linear and non-linear multiple regression models for long-term peak load forecasting and a possible set of affecting factors using historical data gathered from the Electricity and Water Authority (EWA) of the Kingdom of Bahrain. The basic quantity of interest is the monthly peak load in MW.…”

Section: Introductionmentioning

confidence: 99%

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Energy load forecasting: Bayesian and exponential smoothing hybrid methodology

Khorsheed

2019

IJESM

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Purpose The purpose of this study is to present a hybrid approach to model and predict long-term energy peak load using Bayesian and Holt–Winters (HW) exponential smoothing techniques. Design/methodology/approach Bayesian inference is administered by Markov chain Monte Carlo (MCMC) sampling techniques. Machine learning tools are used to calibrate the values of the HW model parameters. Hybridization is conducted to reduce modeling uncertainty. The technique is applied to real load data. Monthly peak load forecasts are calculated as weighted averages of HW and MCMC estimates. Mean absolute percentage error and the coefficient of determination (R2) indices are used to evaluate forecasts. Findings The developed hybrid methodology offers advantages over both individual combined techniques and reveals more accurate and impressive results with R2 above 0.97. The new technique can be used to assist energy networks in planning and implementing production projects that can ensure access to reliable and modern energy services to meet the sustainable development goal in this sector. Originality/value This is original research.

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Section: Introductionmentioning

confidence: 85%

Section: Bayesian Forecastingmentioning

confidence: 99%

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Energy load forecasting: Bayesian and exponential smoothing hybrid methodology

Khorsheed

2019

IJESM

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“…Khorsheed developed four MLR models to predict monthly energy peak demand for the Kingdom of Bahrain, using eight years of historical data. The best model obtained had an R-squared value of 0.94, which incorporated the month and maximum temperature [5]. AL-Hamad and Qamber evaluated two models based on MLR and adaptive neuro-fuzzy inference systems (ANFIS) to predict long term peak demand for six countries in the Gulf Cooperation Council region up to the year 2024 [6].…”

Section: Related Workmentioning

confidence: 99%

“…These entries are also passed through the tanh function Equation (4) that creates a vector of new candidate values to update the cell state. The results of both activation functions are multiplied and then added to update the cell state Equation (5). Lastly, the output gate determines what information of the cell state to output based on Equations ( 6) and (7).…”

Section: Long Short-term Memory Networkmentioning

confidence: 99%

A Deep Learning Approach for Peak Load Forecasting: A Case Study on Panama

Ibrahim

Rabelo

2021

Energies

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Predicting the future peak demand growth becomes increasingly important as more consumer loads and electric vehicles (EVs) start connecting to the grid. Accurate forecasts will enable energy suppliers to meet demand more reliably. However, this is a challenging problem since the peak demand is very nonlinear. This study addresses the research question of how deep learning methods, such as convolutional neural networks (CNNs) and long-short term memory (LSTM) can provide better support to these areas. The goal is to build a suitable forecasting model that can accurately predict the peak demand. Several data from 2004 to 2019 was collected from Panama’s power system to validate this study. Input features such as residential consumption and monthly economic index were considered for predicting peak demand. First, we introduced three different CNN architectures which were multivariate CNN, multivariate CNN-LSTM and multihead CNN. These were then benchmarked against LSTM. We found that the CNNs outperformed LSTM, with the multivariate CNN being the best performing model. To validate our initial findings, we then evaluated the robustness of the models against Gaussian noise. We demonstrated that CNNs were far more superior than LSTM and can support spatial-temporal time series data.

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AI-Empowered Methods for Smart Energy Consumption: A Review of Load Forecasting, Anomaly Detection and Demand Response

Wang,

Bhandari

et al. 2023

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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This comprehensive review paper aims to provide an in-depth analysis of the most recent developments in the applications of artificial intelligence (AI) techniques, with an emphasis on their critical role in the demand side of power distribution systems. This paper offers a meticulous examination of various AI models and a pragmatic guide to aid in selecting the suitable techniques for three areas: load forecasting, anomaly detection, and demand response in real-world applications. In the realm of load forecasting, the paper presents a thorough guide for choosing the most fitting machine learning and deep learning models, inclusive of reinforcement learning, in conjunction with the application of hybrid models and learning optimization strategies. This selection process is informed by the properties of load data and the specific scenarios that necessitate forecasting. Concerning anomaly detection, this paper provides an overview of the merits and limitations of disparate learning methods, fostering a discussion on the optimization strategies that can be harnessed to navigate the issue of imbalanced data, a prevalent concern in power system anomaly detection. As for demand response, we delve into the utilization of AI techniques, examining both incentive-based and price-based demand response schemes. We take into account various control targets, input sources, and applications that pertain to their use and effectiveness. In conclusion, this review paper is structured to offer useful insights into the selection and design of AI techniques focusing on the demand-side applications of future energy systems. It provides guidance and future directions for the development of sustainable energy systems, aiming to serve as a cornerstone for ongoing research within this swiftly evolving field.

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Long-term energy peak load forecasting models: A hybrid statistical approach

Cited by 9 publications

References 14 publications

Energy load forecasting: Bayesian and exponential smoothing hybrid methodology

Energy load forecasting: Bayesian and exponential smoothing hybrid methodology

A Deep Learning Approach for Peak Load Forecasting: A Case Study on Panama

AI-Empowered Methods for Smart Energy Consumption: A Review of Load Forecasting, Anomaly Detection and Demand Response

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