An intelligent hybrid short-term load forecasting model for smart power grids

Raza, Muhammad Qamar; Mithulananthan, Nadarajah; Hung, Duong Quoc; Baharudin, Z.

doi:10.1016/j.scs.2016.12.006

Cited by 76 publications

(23 citation statements)

References 30 publications

(31 reference statements)

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“…This phenomenon shows variations in load curve and peak hour of the entire system. Moreover, the impact of climatic conditions on the load demand in summers is usually more than other time of year [74]. Figure 2 illustrates the load curves for Thursday as an example.…”

Section: Distribution Of Historic Load Datamentioning

confidence: 99%

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

et al. 2018

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Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting.

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Section: Distribution Of Historic Load Datamentioning

confidence: 99%

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

et al. 2018

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“…In this category, articles are presented like BPNN (Li et al [23]), WTBPNN (Changhao et al [24]), GNBPNN (Irani et al [25]), NNPSO (Zhaoyu et al [26]), WT-ANFIS (Karthika et al [27]), ADE-BPNN (Wang et al [28]), Wavelet-PSO-ANFIS,) Catalao et al [29]), and WT-PSO-BPNN (Mandel et al [30]) whose goal is to arrive at the highest accuracy in forecasting. Some other research are found in [10,[31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50]. Figure 1 shows the development of the new technique.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

Salami

Sobhani

Ghazizadeh

2018

Data

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The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section.

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“…The balance of the electrical network and the optimal control of the accumulation system passes through an accurate forecast of load and generation in the network. Load prediction is essential for the efficient and reliable operation of power supply systems, leading to continuous consumer power supply [6]. the PV plant, which according to the forecasting technique used, can receive different types of weather variables as input, which in turn are the output of some Numerical Weather Prediction (NWP) models.…”

Section: Introductionmentioning

confidence: 99%

Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System

Massidda

Marrocu

2017

Energies

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Abstract:The balance between production and consumption in a smart grid with high penetration of renewable sources and in the presence of energy storage systems benefits from an accurate load prediction. A general approach to load forecasting is not possible because of the additional complication due to the increasing presence of distributed and usually unmeasured photovoltaic production. Various methods are proposed in the literature that can be classified into two classes: those that predict by separating the portion of load due to consumption habits from the part of production due to local weather conditions, and those that attempt to predict the load as a whole. The characteristic that should lead to a preference for one approach over another is obviously the percentage of penetration of distributed production. The study site discussed in this document is the grid of Borkum, an island located in the North Sea. The advantages in terms of reducing forecasting errors for the electrical load, which can be obtained by using weather information, are explained. In particular, when comparing the results of different approaches gradually introducing weather forecasts, it is clear that the correct functional dependency of production has to be taken into account in order to obtain maximum yield from the available information. Where possible, this approach can significantly improve the quality of the forecasts, which in turn can improve the balance of a network-especially if energy storage systems are in place.

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An intelligent hybrid short-term load forecasting model for smart power grids

Cited by 76 publications

References 30 publications

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System

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