A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management

Ghasemi, Ali; Shayeghi, Hossein; Moradzadeh, Mohammad; Nooshyar, Mehdi

doi:10.1016/j.apenergy.2016.05.083

Cited by 164 publications

(73 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liu et al [38] applied wavelets and wavelet packets to preprocess the original wind speed data and concluded that the wavelet packet-ANN had the best performance compared with other traditional models. Ghasemi et al [39] proposed a novel hybrid algorithm for electricity price and load forecasting, including the flexible wavelet packet transform (FWPT), conditional mutual information (CMI), artificial bee colony (ABC), support vector machine (SVM) and ARIMA. The results showed that the proposed hybrid algorithm had high accuracy in simultaneous electricity forecasting.…”

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Liu

2016

Energies

View full text Add to dashboard Cite

Abstract:Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids.

show abstract

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Liu

2016

Energies

View full text Add to dashboard Cite

show abstract

“…α, β and δ are initialized as three vectors. The fitness of υ and α, β, δ is calculated by using Equation (24). The crossover is performed by using the equations given below:…”

Section: Hgwdementioning

confidence: 99%

“…Considering this fact, residential appliances were scheduled using an artificial neural network and GA (ANN-GA) scheme in [24]. The scheduling was performed on a single home with four bedrooms on a weekly basis.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

et al. 2018

View full text Add to dashboard Cite

With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30-and 60-min operational time interval (OTI). On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

show abstract

“…The forecast accuracy of these models has been improved by optimizing their input with evolutionary search heuristics, such as Particle Swarm Optimization [4, 32,60,62,63], Genetic Algorithms [22,30,39,42,43,49,54], Simulated Annealing [23,40], Artificial Bee Colony Algorithm [5, 24,47], Differential Evolution [25,57] and Fruit Fly Optimization [38,41]. These hybrid methodologies have been applied to many different fields in forecasting, including tourism flow forecasting [14], electricity demand forecasting [63], rainfall prediction [60], price forecasting [47] and many others.…”

Section: Introductionmentioning

confidence: 99%

ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting

Calster

Baesens

Lemahieu

2017

Applied Soft Computing

View full text Add to dashboard Cite

In forecasting, evolutionary algorithms are often linked to existing forecasting methods to optimize their input parameters. Traditionally, the fitness function of these search heuristics is based on an accuracy measure. In this paper, however, we combine forecasting accuracy with business expertise by defining a flexible and easily interpretable profit function for sales forecasting, which is based on the profit margin of a given product, the volume of its sales and the accuracy of the forecast. ProfARIMA is a new procedure that selects the lags of a Seasonal ARIMA model according to the profit of a model's forecasts by taking advantage of search heuristics. This procedure is tested on both publicly available datasets and a real-life application with datasets of The Coca-Cola Company in order to assess its performance, both in profit and accuracy. Three different evolutionary algorithms were implemented during this testing process, i.e. Genetic Algorithms, Particle Swarm Optimization and Simulated Annealing. The results indicate that ProfARIMA always performs at least equally to the Box-Jenkins methodology and often outperforms this traditional procedure. For The Coca-Cola Company, our new algorithm in combination with Genetic Algorithms even leads to a significantly larger profit for out-of-sample forecasts.

show abstract

A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management

Cited by 164 publications

References 28 publications

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting

Contact Info

Product

Resources

About