Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

Short Term Load Forecasting New Year Celebration Holiday Using Interval Type-2 Fuzzy Inference System (Case Study: Java – Bali Electrical System)

Celebration of New Year In the Indonesian is constituted the one of the visit Indonesian’s tourism. This event course changes the load of electrical energy. The electrical energy providers that control and operation of electrical in Java and Bali (Java, Bali Electrical System) is required to be able to ensure continuity of load demand at this time, and forecast for the future. Short-term load forecasting very need to be supported by computational methods for simulation and validation. The one of computation’s methods is Interval Type – 2 Fuzzy Inference System (IT-2 FIS). Interval Type-2 Fuzzy Inference System (IT-2 FIS) as the development of methods of Interval Type-1 Fuzzy Inference System (IT-1 FIS), it is appropriate to be used in load forecasting because it has the advantages that very flexible on the change of the footprint of uncertainty (FOU), so it supports to establish an initial processing of the time series, computing, simulation and validation of system models. Forecasting methods used in this research are IT-2 FIS. The process for to know and analyzing the peak load a day is the specially day and 4 days before New year Celebration in the previous year continued analysis by using IT-2 FIS will be obtained at the peak load forecasting New Year Celebration in the coming year. This research shown the average of error value in 2012, 2013 and 2014 is 0,642%. This value is better than using the IT-1 FIS which has a value of error to 0.649%. This research concluded that IT-2 FIS can be used in Short Term Load Forecasting

Prediction in Photovoltaic Power by Neural Networks

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches

Evaluating of Short-Term Electrical Load Forecasting System Using Fuzzy Logic Control: A Study Case in Sudan

Generation, Transmission and Distribution sections of the electric power grid system are a function of electric load forecasting. This is because, many benefits can be obtained by using load forecasting, such as reduction in the generating cost and increasing the reliability of the power system due to improving energy management. The objective of this study is therefore to design a fuzzy logic system for short-term electric load forecasting to reduce power losses particularly in times where the electric power generation is higher than the electric load demand. In this study, the independent variables that were applied to the developed short-term load forecasting Simulink model were time, temperature, and similar previous electric day load demand, and they were collected from the specific area load control center in Sudan. Fuzzy rules were prepared using Mamdani implication. The obtained fuzzy logic results were compared with the actual load demand, and it was found that there was an error that ranged between 12% and 0.09%

Short-term electric load forecasting based on a neural fuzzy network

Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

"Can the neuro fuzzy model predict stock indexes better than its rivals?"

This paper develops a model of a trading system by using neuro fuzzy framework in order to better predict the stock index. Thirty well-known stock indexes are analyzed with the help of the model developed here. The empirical results show strong evidence of nonlinearity in the stock index by using KD technical indexes. The trading point analysis and the sensitivity analysis of trading costs show the robustness and opportunity for making further profits through using the proposed nonlinear neuro fuzzy system. The scenario analysis also shows that the proposed neuro fuzzy system performs consistently over time.

USE OF FUZZY LOGIC TO INVESTIGATE WEATHER PARAMETER IMPACT ON ELECTRICAL LOAD BASED ON SHORT TERM FORECASTING

Load forecasting guides the power company to make some decisions on generation, transmission and distribution of electrical power. This work presents a solution methodology, using fuzzy logic approach for short term load forecasting (STLF) for Adamawa State University, Mubi.  The proposed methodology utilized fuzzy reasoning decision rules that use the nonlinear relationships between inputs and outputs. The fuzzy logic model was developed in the Simulink environment of a MATLAB software. The model developed was able to forecast a day ahead load (kW) with a mean absolute error (MAPE) of 6.17% and it was observed that weather parameter (temperature) has significant impact on electrical load.  http://dx.doi.org/10.4314/njt.v35i3.1