Long-term load forecast modelling using a fuzzy logic approach

Ali, Danladi; Yohanna, Michael; Puwu, M.I.; Garkida, B.M.

doi:10.1016/j.psra.2016.09.011

Cited by 57 publications

(37 citation statements)

References 4 publications

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“…Different methods can be used for data forecasting in a microgrid, such as methods based on artificial neural networks (ANNs), to generate wind forecast and power consumption prediction [27,28], which might be extended via short-term load forecasting (STLF) [29], solutions based on neural networks and evolutionary algorithms [30], and adaptive hierarchical genetic algorithm-based neural networks (AHGA-NNs predictor) [31] for wind farms. Other methods are based on fuzzy logic [32], forecast Weibull, and lognormal probability distribution functions, for forecasting wind and solar photovoltaic power output [33], and the least-squares support vector machine (LS-SVM) [34].…”

Section: Methods For Data Forecast In a Microgridmentioning

confidence: 99%

Evaluating the Economic Benefits of a Smart-Community Microgrid with Centralized Electrical Storage and Photovoltaic Systems

et al. 2020

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In this paper, an innovative method for managing a smart-community microgrid (SCM) with a centralized electrical storage system (CESS) is proposed. The method consists of day-ahead optimal power flow (DA-OPF) for day-ahead SCM managing and its subsequent evaluation, considering forecast uncertainties. The DA-OPF is based on a data forecast system that uses a deep learning (DL) long short-term memory (LSTM) network. The OPF problem is formulated as a mathematical mixed-integer nonlinear programming (MINLP) model. Following this, the developed DA-OPF strategy was evaluated under possible operations, using a Monte Carlo simulation (MCS). The MCS allowed us to obtain potential deviations of forecasted data during possible day-ahead operations and to evaluate the impact of the data forecast errors on the SCM, and that of unit limitation and the emergence of critical situations. Simulation results on a real existing rural conventional community endowed with a centralized community renewable generation (CCRG) and CESS, confirmed the effectiveness of the proposed operation method. The economic analysis showed significant benefits and an electricity price reduction for the considered community if compared to a conventional distribution system, as well as the easy applicability of the proposed method due to the CESS and the developed operating systems.Energies 2020, 13, 1764 2 of 21 Centralized and Decentralised Energy Storage SystemFor an SCM, there are two main common options to place storage units-a centralised energy storage system (CESS) and a decentralised energy storage system (DESS). Existing research deals with CESS and DESS, describing their useful properties, limitations, and applications.Generally, energy storage systems (ESSs) are used in microgrids to compensate the intermittent nature of RE, to improve the RE penetration level and to grow the self-consumption rate, reducing dependence on the grid [6]. The CESS represents a central ESS that is directly operated by the energy management system (EMS) of a microgrid. A DESS represents numerous small ESSs allocated in a microgrid. They can be operated by local users, by aggregators, or by the central EMS. The CESS increases the overall efficiency and the resiliency of the microgrid [7], whereas, on the other side, the DESS maximises the local impact of ESSs to adjust to local load profiles [8].According to the study presented in [9], the deployment of a DESS reduces the power losses in the distribution network and can support the load demand with a rapid response [10]. Nonetheless, the deployment of a DESS needs an appropriate mature infrastructure and a control mechanism, both of which are more expensive and complex than those required in the case of a CESS.According to [1], for the same operating conditions of the photovoltaics (PV) generation and ESS capacity, a CESS exhibits a better (of about 17%) reduction of the grid power use, compared to a DESS. In [11], the authors revealed that by using a CESS for the operation of a microgrid, lower operational costs c...

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Section: Methods For Data Forecast In a Microgridmentioning

confidence: 99%

Evaluating the Economic Benefits of a Smart-Community Microgrid with Centralized Electrical Storage and Photovoltaic Systems

et al. 2020

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“…The values of variables can be approximate between 0 and 1 depending on reasoning. The main notion of fuzzy set was initially introduced by Lotfi Zadeh in 1965 (Ali et al, 2016). The theory of fuzzy set can be regarded as a generalized classical set theory (Jantakoon, 2016).…”

Section: Fuzzy System (Fss)mentioning

confidence: 99%

The Prediction of Solar Radiation Using Fuzzy Logic: a Case Study

MOHAMMED

2018

juod

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Solar energy is used in many applications such as producing agricultural food, renewable energy, and heating and lighting systems… etc. Nowadays, countries all over the world, especially the developing countries are facing a great challenge which is providing sustainable energy for consumers. Electricity is the most common type of energy that is used by consumers in which oil or nuclear power is used to produce sufficient amount of electricity for the constant increase of the population in the present. However, both oil and nuclear energy negatively affect the global warming; therefore, solar energy is aspired by many countries to decrease the effects of the global warming and produce renewable sources of energy. The aim of this study is to predict the use of solar radiation for solar energy to produce electricity in Duhok city due to the fact that "national electricity" is not enough for the great number of consumers; as a result, people depend on "local or private generators" which mainly depend on oil to produce electricity. Fuzzy logic approach is used to estimate the solar radiation. The four fuzzy systems are created using the available data in Duhok City in 2016. Daily observations for temperature, humidity and wind speed for four seasons are analyzed to estimate the solar radiation. The predicted outputs of fuzzy logic system are compared with the actual solar radiation. In addition, the fuzzy system approach is evaluated using Mean Absolute Percentage Error (MAPE) and Absolute Percentage Error (APE). The outcomes of MAPE and APE are 5. 86%, 1.54%, 2.76% and 1.52 for four seasons (winter, summer, spring and fall), respectively. According to the results, the performance of fuzzy system is reasonably effective in predicting the solar radiation.

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“…Artificial intelligence techniques are used in the prediction system. In load forecasting, the prediction is typically divided into short term load forecasting (STLF), medium term load forecasting (MTLF), and long term load forecasting (LTLF) [21][22][23]. LTLF is widely used today to decide when it is necessary to upgrade existing electricity distribution systems and build new lines or substations.…”

Section: Forewordmentioning

confidence: 99%

The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

2017

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Electricity demand shifting and reduction still raise a huge interest for end-users at the household level, especially because of the ongoing design of a dynamic pricing approach. In particular, end-users must act as the starting point for decreasing their consumption during peak hours to prevent the need to extend the grid and thus save considerable costs. This article points out the relevance of a fuzzy logic algorithm to efficiently predict short term load consumption (STLC). This approach is the cornerstone of a new home energy management (HEM) algorithm which is able to optimize the cost of electricity consumption, while smoothing the peak demand. The fuzzy logic modeling involves a strong reliance on a complete database of real consumption data from many instrumented show houses. The proposed HEM algorithm enables any end-user to manage his electricity consumption with a high degree of flexibility and transparency, and "reshape" the load profile. For example, this can be mainly achieved using smart control of a storage system coupled with remote management of the electric appliances. The simulation results demonstrate that an accurate prediction of STLC gives the possibility of achieving optimal planning and operation of the HEM system.

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Long-term load forecast modelling using a fuzzy logic approach

Cited by 57 publications

References 4 publications

Evaluating the Economic Benefits of a Smart-Community Microgrid with Centralized Electrical Storage and Photovoltaic Systems

Evaluating the Economic Benefits of a Smart-Community Microgrid with Centralized Electrical Storage and Photovoltaic Systems

The Prediction of Solar Radiation Using Fuzzy Logic: a Case Study

The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

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