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Fuzzy Logic Approach for Forecasting Half-hourly Electricity Load Demand
... In addition to the above discussed research, fuzzy logic systems have also been implemented in other studies [107][108][109][110][111]. ...
The smart grid concept is introduced to accelerate the operational efficiency and enhance the reliability and sustainability of power supply by operating in self-control mode to find and resolve the problems developed in time. In smart grid, the use of digital technology facilitates the grid with an enhanced data transportation facility using smart sensors known as smart meters. Using these smart meters, various operational functionalities of smart grid can be enhanced, such as generation scheduling, real-time pricing, load management, power quality enhancement, security analysis and enhancement of the system, fault prediction, frequency and voltage monitoring, load forecasting, etc. From the bulk data generated in a smart grid architecture, precise load can be predicted before time to support the energy market. This supports the grid operation to maintain the balance between demand and generation, thus preventing system imbalance and power outages. This study presents a detailed review on load forecasting category, calculation of performance indicators, the data analyzing process for load forecasting, load forecasting using conventional meter information, and the technology used to conduct the task and its challenges. Next, the importance of smart meter-based load forecasting is discussed along with the available approaches. Additionally, the merits of load forecasting conducted using a smart meter over a conventional meter are articulated in this paper.
... In artificial intelligencebased modeling, it involved the models, which represent human intelligence or knowledge. It consists of the neural network [20]- [22], fuzzy time series [3], [23]- [25], fuzzy rule-based system [26]- [28] support vector machine [29], and deep learning [30]. Meanwhile, combining forecasting methods to form a single forecasting method is known as the hybrid method. ...
... In the literatures many techniques of forecasting have been evolved. These comprise artificial neural network (ANN), the ARMA model, exponential smoothing, data mining models, linear regression and fuzzy logic (Filik et al., 2011;Ismail et al., 2011). Amongst these techniques, ANN and fuzzy logic are widely utilized. ...
... Besides, Ali and his colleagues have deployed the fuzzy logic models to the short-term ELF [110], and long-term load forecasting [111]. The ELF forecasting was also addressed in works [112][113][114][115]. Further, Jamaaluddin et al. [116] were dealing with a very short-term load forecasting of a peak load time by using the fuzzy logic, while Jagbir and Singh [117] have conducted an FL-based short-term load forecasting model for a 220 kV transmission line. ...
Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic interest. Hence, the accuracy of electric load forecasting has great importance for energy generating capacity scheduling and power system management. This paper presents a review of forecasting methods and models for electricity load. About 45 academic papers have been used for the comparison based on specified criteria such as time frame, inputs, outputs, the scale of the project, and value. The review reveals that despite the relative simplicity of all reviewed models, the regression analysis is still widely used and efficient for long-term forecasting. As for short-term predictions, machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are favored.
... In the literatures many techniques of forecasting have been evolved. These comprise artificial neural network (ANN), the ARMA model, exponential smoothing, data mining models, linear regression and fuzzy logic (Filik et al., 2011;Ismail et al., 2011). Amongst these techniques, ANN and fuzzy logic are widely utilized. ...
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.
... In the literatures many techniques of forecasting have been evolved. These comprise artificial neural network (ANN), the ARMA model, exponential smoothing, data mining models, linear regression and fuzzy logic (Filik et al., 2011;Ismail et al., 2011). Amongst these techniques, ANN and fuzzy logic are widely utilized. ...
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.
Facing development requirements and changes in the global energy context, Morocco has begun a process of diversification of the national energy mix in favor of renewable energy, while ensuring a competitive energy, in terms of costs, availability of products and their security and sustainability. Within this framework, Morocco launched in 2009 a national energy strategy whose major orientations focus on the security of energy supply and the generalization of its access, the preservation of the environment, through the use of renewable energy, energy efficiency, the strengthening of interconnection and regional cooperation. Through this article, the current state of the Moroccan network will be studied, as well as its potential in terms of renewable energy. Some strategies to overcome the challenges facing smart grid deployment in Morocco will also be presented. Then, the long-term energy demand, generation capacity, and renewable energy evolution in Morocco around 2030 will be estimated based on a time series using the artificial neural network method, which can be injected into the grid without causing any transit restrictions on the utility network or on the whole power system. As a result, the wind power available capacity was estimated to be 4087 MW, and the solar power available capacity was estimated to be 4713 MW by 2030. These results will be then compared to those estimated with the mathematical method. As well as, with the accuracy results of similar studies with different time series forecasting techniques. The accuracy value of this study is between 1.2% and 3.5%. So, the performance and viability of the proposed model can be studied.
Global Power Prediction Systems prototype version 2021 is presented with its system decomposition, scope, geographical/administrative/power grid decompositions, and similar. “Welcome”, “sign-up”, “log-in”, and “non-registered user main” web-interfaces are designed as draft on Quant UX. Map canvas is given as world political map with/without world power grid layers on QGIS 3.16.7-Hannover. Data input file is prepared based on several sources (1971-2018). It includes minimum and maximum values due to source value differences. 70/30 principle is applied for train/test splitting (training/testing sets: 1971-2003/2004-2018). 10 models are prepared on R version 4.1.1 with RStudio 2021.09.0+351. These are R::base(lm), R::base(glm), R::tidymodels::parsnip(engine("lm")), R::tidymodels::parsnip(engine("glmnet")) with lasso regularization, R::tidymodels::parsnip(engine("glmnet")) with ridge regularization, R::forecast(auto.arima) auto autoregressive integrated moving average (ARIMA), R::forecast(arima) ARIMA(1,1,2), and ARIMA(1,1,8). Electricity demand in kilowatt-hours at the World level zone for up to 500-years (2019-2519) prediction period with only 1-year interval is forecasted. The best model is the auto ARIMA (mean absolute percentage error MAPE and symmetric mean absolute percentage error SMAPE for minimum and maximum electricity consumption respectively 1,1652; 6,6471; 1,1622; 6,9043). Ex-post and ex-ante plots with 80%-95% confidence intervals are prepared in R::tidyverse::ggplot2. There are 3 alternative scripts (long, short, RStudio Cloud). Their respective runtimes are 41,45; 25,44; and 43,33 seconds. Ex-ante 500-year period (2019-2519) is indicative and informative.
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