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Book part
Publication date: 9 September 2024

Muhammad Hassan Raza

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The Multilevel Community Engagement Model
Type: Book
ISBN: 978-1-83797-698-0

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Article
Publication date: 20 November 2009

Sanjeev Kumar Aggarwal, L.M. Saini and Ashwani Kumar

Several research papers related to electricity price forecasting have been reported in the leading journals in last 20 years. The purpose of this paper is to present a…

1272

Abstract

Purpose

Several research papers related to electricity price forecasting have been reported in the leading journals in last 20 years. The purpose of this paper is to present a comprehensive survey and comparison of these techniques.

Design/methodology/approach

The present article provides an overview of the statistical short‐term price forecasting (STPF) models. The basic theory of these models, their further classification and their suitability to STPF has been discussed. Quantitative evaluation of the performance of these models in the framework of accuracy achieved and computation time taken has been performed. Some important observations of the literature survey and key issues regarding STPF methodologies are analyzed.

Findings

It has been observed that price forecasting accuracy of the reported models in day‐ahead markets is better as compared to that in real time markets. From a comparative analysis perspective, there is no hard evidence of out‐performance of one model over all other models on a consistent basis for a very long period. In some of the studies, linear models like dynamic regression and transfer function have shown superior performance as compared to non‐linear models like artificial neural networks (ANNs). On the other hand, recent variations in ANNs by employing wavelet transformation, fuzzy logic and genetic algorithm have shown considerable improvement in forecasting accuracy. However more complex models need further comparative analysis.

Originality/value

This paper is intended to supplement the recent survey papers, in which the researchers have restricted the scope to a bibliographical survey. Whereas, in this work, after providing detailed classification and chronological evolution of the STPF techniques, a comparative summary of various price‐forecasting techniques, across different electricity markets, is presented.

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International Journal of Energy Sector Management, vol. 3 no. 4
Type: Research Article
ISSN: 1750-6220

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Article
Publication date: 21 November 2008

S.K. Aggarwal, L.M. Saini and Ashwani Kumar

Price forecasting is essential for risk management in deregulated electricity markets. The purpose of this paper is to propose a hybrid technique using wavelet transform (WT) and…

845

Abstract

Purpose

Price forecasting is essential for risk management in deregulated electricity markets. The purpose of this paper is to propose a hybrid technique using wavelet transform (WT) and multiple linear regression (MLR) to forecast price profile in electricity markets.

Design/methodology/approach

Price series is highly volatile and non‐stationary in nature. In this work, initially complete price series has been decomposed into separate 48 half‐hourly series and then these series have been categorized into different segments for price forecasting. For some segments, WT based MLR has been applied and for the other segments, simple MLR model has been applied. The model is general in nature and has been implemented for one day‐ahead price forecasting in National Electricity Market (NEM) of Australia. Participants can use the technique practically, since it predicts price well before submission of bids.

Findings

Forecasting performance of the proposed WT and MLR based mixed model has been compared with the three other models, an analytical model, a MLR model and an artificial neural network (ANN) based model. The proposed model was found to be better. Performance evaluation for different wavelets was performed, and it has been observed that for improving forecasting accuracy using WT, Daubechies wavelet of order two gives the best performance.

Originality/value

Forecasting accuracy improvement of an established technique by incorporating time domain and wavelet domain variables of the same time series into one set has been implemented in this work. The paper also attempts to explain how non‐stationarity can be removed from a non‐stationary time series by applying WT after appropriate statistical investigation. Moreover, real time electricity markets are highly unpredictable and yet under investigated. The model has been applied to NEM for the same reason.

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International Journal of Energy Sector Management, vol. 2 no. 4
Type: Research Article
ISSN: 1750-6220

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Article
Publication date: 10 August 2010

Shamsuddin Ahmed

The proposed algorithm successfully optimizes complex error functions, which are difficult to differentiate, ill conditioned or discontinuous. It is a benchmark to identify…

252

Abstract

Purpose

The proposed algorithm successfully optimizes complex error functions, which are difficult to differentiate, ill conditioned or discontinuous. It is a benchmark to identify initial solutions in artificial neural network (ANN) training.

Design/methodology/approach

A multi‐directional ANN training algorithm that needs no derivative information is introduced as constrained one‐dimensional problem. A directional search vector examines the ANN error function in weight parameter space. The search vector moves in all possible directions to find minimum function value. The network weights are increased or decreased depending on the shape of the error function hyper surface such that the search vector finds descent directions. The minimum function value is thus determined. To accelerate the convergence of the algorithm a momentum search is designed. It avoids overshooting the local minimum.

Findings

The training algorithm is insensitive to the initial starting weights in comparison with the gradient‐based methods. Therefore, it can locate a relative local minimum from anywhere of the error surface. It is an important property of this training method. The algorithm is suitable for error functions that are discontinuous, ill conditioned or the derivative of the error function is not readily available. It improves over the standard back propagation method in convergence and avoids premature termination near pseudo local minimum.

Research limitations/implications

Classifications problems are efficiently classified when using this method but the complex time series in some instances slows convergence due to complexity of the error surface. Different ANN network structure can further be investigated to find the performance of the algorithm.

Practical implications

The search scheme moves along the valleys and ridges of the error function to trace minimum neighborhood. The algorithm only evaluates the error function. As soon as the algorithm detects flat surface of the error function, care is taken to avoid slow convergence.

Originality/value

The algorithm is efficient due to incorporation of three important methodologies. The first mechanism is the momentum search. The second methodology is the implementation of directional search vector in coordinate directions. The third procedure is the one‐dimensional search in constrained region to identify the self‐adaptive learning rates, to improve convergence.

Details

Kybernetes, vol. 39 no. 7
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 16 August 2021

Farhad Khosrojerdi, Okhaide Akhigbe, Stéphane Gagnon, Alex Ramirez and Gregory Richards

The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are…

822

Abstract

Purpose

The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects.

Design/methodology/approach

A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects?

Findings

The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control.

Originality/value

Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources.

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International Journal of Energy Sector Management, vol. 16 no. 2
Type: Research Article
ISSN: 1750-6220

Keywords

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Article
Publication date: 3 November 2020

Yasemın Özyer and Safiye Yanmış

This review, which was created specifically for cancer care by reviewing the literature, was prepared to provide suggestions for determining the current difficulties in cancer…

230

Abstract

Purpose

This review, which was created specifically for cancer care by reviewing the literature, was prepared to provide suggestions for determining the current difficulties in cancer care during COVID-19 outbreak and managing these difficulties. It is thought that this review can contribute to the identification of deficiencies related to the subject, to create interest in this issue in our country and in the world and to guide patients in the care during the COVID-19 pandemic process.

Design/methodology/approach

The literature search was carried out using the following electronic seven databases. Search terms used included: “COVID-19”, “management of cancer care” and “cancer care”. Articles meeting the following criteria were included in the current review: articles published in English, articles published in peer-reviewed journals and articles and guidelines published in 2020, articles suggesting management of cancer care during the COVID-19 outbreak.

Findings

The findings suggest that new guidelines need to be created to assess the level of problems in cancer treatment and in hospital, to respond appropriately with the best available resources during COVID-19 outbreak.

Originality/value

This paper seeks to provide suggestions for determining the current difficulties in cancer care during COVID-19 outbreak and managing these difficulties. By doing so, it is believed the suggestions presented will contribute significantly to the quality of cancer care during COVID-19 outbreak.

Details

International Journal of Emergency Services, vol. 10 no. 1
Type: Research Article
ISSN: 2047-0894

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Article
Publication date: 8 February 2021

Emrah Dokur, Cihan Karakuzu, Uğur Yüzgeç and Mehmet Kurban

This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural parameters by…

338

Abstract

Purpose

This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural parameters by using a forecasting process.

Design/methodology/approach

The modelling performance of the Meta-ELM architecture varies depending on the network parameters it contains. The choice of Meta-ELM parameters is important for the accuracy of the models. For this reason, the optimal choice of Meta-ELM parameters is investigated on the problem of wind speed forecasting in this paper. The hourly wind-speed data obtained from Bilecik and Bozcaada stations in Turkey are used. The different number of ELM groups (M) and nodes (Nh) are analysed for determining the best modelling performance of Meta-ELM. Also, the optimal Meta-ELM architecture forecasting results are compared with four different learning algorithms and a hybrid meta-heuristic approach. Finally, the linear model based on correlation between the parameters was given as three dimensions (3D) and calculated.

Findings

It is observed that the analysis has better performance for parameters of Meta-ELM, M = 15 − 20 and Nh = 5 − 10. Also considering the performance metric, the Meta-ELM model provides the best results in all regions and the Levenberg–Marquardt algorithm -feed forward neural network and adaptive neuro fuzzy inference system -particle swarm optimization show competitive results for forecasting process. In addition, the Meta-ELM provides much better results in terms of elapsed time.

Originality/value

The original contribution of the study is to investigate of determination Meta-ELM parameters based on forecasting process.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 40 no. 3
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 27 May 2022

Faruk Yalcin, Ugur Arifoglu and Irfan Yazici

This paper aims to present the design and implementation of a new general-purpose single-phase buck-type inverter.

157

Abstract

Purpose

This paper aims to present the design and implementation of a new general-purpose single-phase buck-type inverter.

Design/methodology/approach

The operation of the proposed inverter is based on the general-purpose buck converter. The proposed buck-type inverter topology is designed with reduced numbers of passive and active elements to minimize design cost and complexity. Also, an efficient hybrid control technique based on the proportional‐integral‐derivative (PID) supported by open-loop control signal is offered for the control of the proposed inverter. The proposed hybrid control method improves the performance of the PID controller during the change of inverter operation parameters. A close to single-phase sine wave output voltage with low total harmonic distortion (THD) can be produced by the proposed inverter in a wide range of voltage and frequency lower than the inverter input voltage value.

Findings

Simulation and experimental test studies are applied to the proposed inverter. The experimental laboratory setup is built for 0–50 Hz, 0–100 Vp, 0.5 kW. Both the simulation and the experimental test results show that the single-phase inverter can produce close to sine wave output voltage with THD level under 5% in a wide range of frequency for various operating conditions and for different loads.

Originality/value

In this paper, a new topology and a new hybrid control technique that are patented by the corresponding author are implemented for a single-phase buck-type inverter through a scientific project. The operating results of the study reveal the efficient operating capability with a simple topology structure.

Details

Circuit World, vol. 48 no. 3
Type: Research Article
ISSN: 0305-6120

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Article
Publication date: 8 February 2019

Sanjita Jaipuria and Siba Sankar Mahapatra

The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period…

659

Abstract

Purpose

The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period order-up-to level ((R, S)) inventory control policy and its different variants such as (R, βS) (R, γO) and (R, γO, βS) proposed by Jakšič and Rusjan, (2008) and Bandyopadhyay and Bhattacharya (2013).

Design/methodology/approach

A hybrid forecasting model has been developed by combining the feature of discrete wavelet transformation (DWT) and an intelligence technique, multi-gene genetic programming (MGGP), denoted as DWT-MGGP. Performance of DWT-MGGP model has been verified under (R, S) inventory control policy considering demand from three different manufacturing companies.

Findings

A comparison between DWT-MGGP model and autoregressive integrated moving average forecasting model has been done by estimating forecast error and BWE. Further, this study has been extended with analysing the behaviour of BWE considering different variants of (R, S) policy such as (R,βS) (R, γO) and (R,γO,βS) and found that BWE can be moderated by controlling the inventory smoothing (β) and order smoothing parameters (γ).

Research limitations/implications

This study is limited to different variants of (R, S) inventory control policy. However, this study can be further extended to continuous review policy.

Practical implications

The proposed DWT-MGGP model can be used as a suitable demand forecasting model to control the BWE when (R, S), (R,βS) (R,γO) and (R,γO,βS)inventory control policies are followed for replenishment.

Originality/value

This study analyses the behavior of BWE through controlling the inventory smoothing (β) and order smoothing parameters (γ) when demand is predicted using DWT-MGGP forecasting model and order is estimated using (R, S), (R,βS) (R,γO) and (R,γO,βS) inventory control policies.

Details

Journal of Modelling in Management, vol. 14 no. 2
Type: Research Article
ISSN: 1746-5664

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Article
Publication date: 25 September 2020

Christof Naumzik and Stefan Feuerriegel

Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely…

319

Abstract

Purpose

Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, the purpose of this paper is to provide accurate forecasts..

Design/methodology/approach

This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, this paper implements a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis.

Findings

This study disentangles the respective relevance of each predictor. This study shows that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior.

Research limitations/implications

The performance gain from including more predictors might be larger than from a better model. Future research should place attention on expanding the data basis in electricity price forecasting.

Practical implications

When developing pricing models, practitioners can achieve reasonable performance with a simple model (e.g. seasonal-autoregressive moving-average) that is built upon a wide range of predictors.

Originality/value

The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.

Details

International Journal of Energy Sector Management, vol. 15 no. 1
Type: Research Article
ISSN: 1750-6220

Keywords

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