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A functional time series is the realization of a stochastic process where each observation is a continuous function defined on a finite interval. These processes are commonly found in electricity markets and are gaining more importance as more market data become available and markets head toward continuous-time marginal pricing approaches. Forecasting these time series requires models that operate...
The ability to operate effectively on electricity spot markets relies on the capability to devise appropriate bidding strategies. These in turn can benefit from the inclusion of a reliable forecast of short term system marginal prices (SMPs). In a market with an increasing percentage of renewable generators, reliable forecasts must necessarily consider additional factors such as meteorological forecasts,...
Deregulation of wholesale energy markets has brought many challenges and one of them is the accuracy of price forecasting methods. In the environment where price is set not only by matching supply (generation side) and demand (utilities and customer side) but is also influenced by various factors such as capacity, ancillary services and transmission congestion, the most popular forecasting tools are...
Electricity price forecasting models are of great importance for market participants due to their considerable volatility, especially in deregulated and competitive contexts. As a result, these models are highly demanded, especially in day-today applications, which require not only accurate results, but also fast responsiveness. Taking these needs into account, this work proposes a novel short-term...
Limitations of the existing methods and models for uneven energy consumption forecast are defined. The energy consumption planning method based on informational technologies, the probability theory, the game theory, hour energy prices and volumes relations is offered. All possible variants of prices and volumes relations are identified. Two utility functions for positive and negative correction of...
In this paper, an ensemble learning model, namely the random forest (RF) model, is used to predict both the exact values as well as the class labels of 24 hourly prices in the California Independent System Operator (CAISO)'s day-ahead electricity market. The focus is on predicting the prices for the Pacific Gas and Company (PG&E) default load aggregation point (DLAP). Several effective features,...
Forecasting hourly spot prices for real-time electricity usage is a challenging task. This paper investigates a series of forecasting methods to 90 and 180 days of load data collection acquired from the Iberian Electricity Market (MIBEL). This dataset was used to train and test multiple forecast models. The Mean Absolute Percentage Error (MAPE) for the proposed Hybrid combination of Auto Regressive...
Commodity and electricity price models are motivated by the several unexpected evolutions that commodity prices have shown over the previous decades. Several models are based on the classic Black-Scholes model, which was one of the first to simulate the stochastic behaviour of commodity prices. However, as of today, these forecasting models show poor performance when tested in long-term horizons,...
The subsidy feed in tariff policy, which has been adopted in the past few years, for accelerating renewable energy investments, cannot be retained as a sustainable business model for the future smart energy grid. This is mainly due to the fact that this policy increases the energy cost, especially when the amount of the energy generated by renewable sources is not negligible compared to the traditional...
In the literature, there is a wide agreement that electricity market time series include several components describing the long-term dynamics, annual, weekly and daily periodicities, calendar effects, jumps, and so on. As a result, modeling electricity variables requires the estimation of these components to filter them out and achieve stationarity, and to project them into the future. For some of...
This paper presents a hybrid model for electricity price forecasting with focus on price spikes predictions. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive spot electricity markets. A two-layered model is introduced for forecasting 7-days ahead hourly electricity price values of electricity spot market. Due to the importance of improved analysis...
This paper analyzes the volatility transmissions between the different sessions of the Spanish Intraday electricity market for the period 2002–2013 using hourly prices. Based on the daily realized volatility and the organization of the market, volatility can only be transmitted from session 1 to 6 in chronological order, which allows to formulate six Autoregressive Distributed Lag models in which...
Efficient modeling and forecasting for the electricity demand is an important issue in competitive electricity market. In most electricity markets the daily demand is determined the day before the delivery by means of (semi-)hourly auctions for the following day. Therefore, adequate and reliable day-ahead demand forecasts are very important. In this paper, the forecasting performances of parametric...
Although the techniques for normal price prediction are well advanced, price spike prediction remains a challenge. The aim of this paper is to present a method to predict both normal day-ahead electricity prices as well as day-ahead price spikes using classification and regression trees. The models are build using historical data of European electricity market and transmission system variables. The...
This paper reports the research that was developed to predict biding curves submitted by generation players to the Market Operator of the Iberian Electricity Market. In this scope, we used a data set based on publicly available information from the website of the Market Operator to develop a two-step ANN prediction model. The first step involves the prediction of the amount of energy bidden at zero...
This paper proposes a new hybrid approach for short-term energy price prediction. This approach combines ARIMA and NN models in a cascaded structure and uses explanatory variables. A two step procedure is applied. In the first step, the explanatory variables are predicted. In the second one, the energy prices are forecasted by using the explanatory variables prediction. The prediction time horizon...
In the electricity market, the price as the lever results in the dramatic variations, especially, the capacity or willingness of electricity consumers and then demand may be low, particularly over short time frames. Therefore demand-side management (DSM) has been put into practice, and the market supervisors become more and more focused on the price dynamics of the short-term, because of its effects...
The Iberian Electricity Market (MIBEL) emerges in the context of the integration and cooperation between the Portuguese and Spanish electricity markets, in response to the European Union incentive for regional electricity markets creation. The present study, focus on the modeling and forecasting of the hourly competitive strategies of the electricity producers in the MIBEL. For this analysis, the...
The generating companies as well as the bulk sellers in a deregulated environment want to maximize their profits. For these entities, offering the appropriate amount of electricity at the right time with the right bidding price is of paramount importance. A comparison between support vector machine (SVM) and least squares support vector machine (LSSVM) in mid-term electricity MCP forecasting is presented...
This paper proposes a recurrent neural network model for the day ahead deregulated electricity market price forecasting that could be realized using the Elman network. In a deregulated market, electricity price is influenced by many factors and exhibits a very complicated and irregular fluctuation. Both power producers and consumers need a single compact and robust price forecasting tool for maximizing...
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