Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting

Year-ahead forecasting of electricity prices is an important issue in the current context of electricity markets. Nevertheless, only one-day-ahead forecasting is commonly tackled up in previous published works. Moreover, methodology developed for the short-term does not work properly for long-term forecasting. In this paper we provide a seasonal extension of the Non-Stationary Dynamic Factor Analysis, to deal with the interesting problem (both from the economic and engineering point of view) of long term forecasting of electricity prices. Seasonal Dynamic Factor Analysis (SeaDFA) allows to deal with dimensionality reduction in vectors of time series, in such a way that extracts common and specific components. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal one, by means of common factors following a multiplicative seasonal VARIMA(p,d,q)×(P,D,Q)s model. Besides, a bootstrap procedure is proposed to be able to make inference on all the parameters involved in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing to enhance the coverage of forecast confidence intervals. Concerning the innovative and challenging application provided, bootstrap procedure developed allows to calculate not only point forecasts but also forecasting intervals for electricity prices

Application of Kalman Filtering for PV Power Prediction in Short-Term Economic Dispatch

The aim of this thesis is to predict the short-term power production of PhotoVoltaic (PV) power plants for the economic dispatch problem with the help of Kalman filtering. The Economic Dispatch (ED) problem in power systems is known as an optimization problem in which the cost of producing energy to reliably supply consumers is minimized, hence the power production is assigned to all the generating units that are dispatchable. Because of the generation cost of renewable energy such as PV is relatively low, it is advantageous to utilize. However, these resources are intermittent. These renewable resources bring a lot of uncertainty into the power system, their power cannot be pre-specified due to their weather dependent properties and therefore it is a big challenge to include them in the ED problem.;For this reason, the work in this thesis will focus on developing a predictive model built on Kalman Filtering for the short-term PV prediction. The model first predicts the solar irradiance and temperature based on an initial guess at each time period. Then, the Kalman filter will refine the results using sensor measurements so that the final estimated outputs from this filter can be used for better prediction in the next period. The PV electric power is then calculated since it is a function of irradiance and temperature.;The proposed methodology has been illustrated using the IEEE 24-bus reliability test system. The real data from National Renewable Energy Laboratory is used in this thesis as the actual outputs that the outputs of the predicting model should get close to. Finally, the performance of the proposed approach is obtained by comparing its results with the results from an available method called the persistent prediction method

Short-term electricity prices forecasting in a competitive market: A neural network approach

This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California

Load/Price Forecasting and Management of Demand Response for Smart Grids: Methodologies and Challenges

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Aggregating Prophet and Seasonal Trend Decomposition for Time Series Forecasting of Italian Electricity Spot Prices

The cost of electricity and gas has a direct influence on the everyday routines of people who rely on these resources to keep their businesses running. However, the value of electricity is strongly related to spot market prices, and the arrival of winter and increased energy use owing to the demand for heating can lead to an increase in energy prices. Approaches to forecasting energy costs have been used in recent years; however, existing models are not yet robust enough due to competition, seasonal changes, and other variables. More effective modeling and forecasting approaches are required to assist investors in planning their bidding strategies and regulators in ensuring the security and stability of energy markets. In the literature, there is considerable interest in building better pricing modeling and forecasting frameworks to meet these difficulties. In this context, this work proposes combining seasonal and trend decomposition utilizing LOESS (locally estimated scatterplot smoothing) and Facebook Prophet methodologies to perform a more accurate and resilient time series analysis of Italian electricity spot prices. This can assist in enhancing projections and better understanding the variables driving the data, while also including additional information such as holidays and special events. The combination of approaches improves forecast accuracy while lowering the mean absolute percentage error (MAPE) performance metric by 18% compared to the baseline model