Short term load forecasting based on hybrid artificial neural networks and particle swarm optimisation

Short term load forecasting (STLF) is the prediction of electrical load for a period that ranges from the next minute to a week. The main objectives of the STLF function are to predict future load for the generation scheduling at power stations; assessment of the security of the power system as well as for timely dispatching of electrical power. STLF is primarily required to determine the most economic manner in which an electrical utility can schedule generation resources without compromising on the reliability requirements, operational constraints, policies and physical environmental and equipment limitations. Another application of the STLF is for predictive assessment of the power system security. This system load forecast is an essential data requirement for off-line network analysis in order to determine conditions under which a system may become vulnerable. This information allows the dispatcher to prepare the necessary corrective actions. The third application of STLF is to provide the system dispatcher with more recent information i.e., the most recent forecast with the latest weather prediction and random behaviour taken into account. The dispatcher needs this information to operate the system economically and reliably. Due to the sensitivities surrounding a load forecast, it thus becomes crucial that the forecasting error is minimised. There are various methods that are used for short term load forecasting, namely; statistical methods and computational intelligence methods. Statistical methods are known as the regression methods which forecast the future electrical load based on historic time series load information. These methods have been in use for many years however due to the dynamic changes in the power system today such as the introduction of Independent Power Producers (IPPs) onto the grid; it becomes difficult to use these methods because they are very static and inflexible i.e. they cannot be manipulated by including rules or expert knowledge in order to counter the effect of any sudden changes in the power system. Their inability to adapt to the changing behaviour of the power system thus leads to high forecasting errors. Computational intelligence (CI) methods however are dynamic and are able to learn by experience. Short term load forecasts have been conducted by using various CI methods such as Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Fuzzy Logic (FL), Expert Systems (ES), and Particle Swarm Optimisation (PSO). Hybrid versions of these methods, where two or more CI methods are amalgamated in a process to forecast future load, have also been used. iv In this research, a traditional forecasting technique, Multiple Linear Regression (MLR), was compared with a CI technique, Artificial Neural Networks. ANN was also compared with another neural network method namely Elman Recurrent Neural Network (ERNN) to determine whether a more neural network method with memory yields better results as compared to ANN

A comparison of univariate methods for forecasting electricity demand up to a day ahead

This empirical paper compares the accuracy of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead. The very short lead times are of particular interest as univariate methods are often replaced by multivariate methods for prediction beyond about six hours ahead. The methods considered include the recently proposed exponential smoothing method for double seasonality and a new method based on principal component analysis (PCA). The methods are compared using a time series of hourly demand for Rio de Janeiro and a series of half-hourly demand for England and Wales. The PCA method performed well, but, overall, the best results were achieved with the exponential smoothing method, leading us to conclude that simpler and more robust methods, which require little domain knowledge, can outperform more complex alternatives

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

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