https://www.lef.wiwi.uni-due.de/fileadmin/fileupload/BWL-LEF/Seminarreihe/RWeron14_Essen.pdf

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures and (iii) statistical testing of the significance of the outperformance of one model by another.

This paper proposes a novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models. The historical and usually ill-behaved price series is decomposed using the wavelet transform in a set of better-behaved constitutive series. Then, the future values of these constitutive series are forecast using properly fitted ARIMA models. In turn, the ARIMA forecasts allow, through the inverse wavelet transform, reconstructing the future behavior of the price series and therefore to forecast prices. Results from the electricity market of mainland Spain in year 2002 are reported.

Day-ahead electricity price forecasting using the wavelet transform and ARIMA models

Day-ahead ,melectricity price forecasting using the wavelet transform and arima models

This paper presents a novel appliance commitment algorithm that schedules thermostatically controlled household loads based on price and consumption forecasts considering users' comfort settings to meet an optimization objective such as minimum payment or maximum comfort. The formulation of an appliance commitment problem is described using an electrical water heater load as an example. The thermal dynamics of heating and coasting of the water heater load is modeled by physical models; random hot water consumption is modeled with statistical methods. The models are used to predict the appliance operation over the scheduling time horizon. User comfort is transformed to a set of linear constraints. Then, a novel linear-sequential-optimization-enhanced, multiloop algorithm is used to solve the appliance commitment problem. The simulation results demonstrate that the algorithm is fast, robust, and flexible. The algorithm can be used in home/building energy-management systems to help household owners or building managers to automatically create optimal load operation schedules based on different cost and comfort settings and compare cost/benefits among schedules.

Appliance Commitment for Household Load Scheduling

Bidding competition is one of the main transaction approaches in deregulated electricity markets. Locational marginal prices (LMPs) resulting from bidding competition represent electricity values at nodes or in areas. A method using both neural networks (NNs) and fuzzy-c-means (FCM) is presented for forecasting LMPs. The recurrent neural network (RNN) was addressed and the traditional NN-based on a backpropagation algorithm was also investigated for comparison. The FCM helped classify the load levels into three clusters. Individual RNNs according to three load clusters were developed for forecasting LMPs. These RNNs were trained/ validated and tested with historical data from the PJM (Pennsylvania, New Jersey, and Maryland) power system. It was found that the proposed neural networks were capable of forecasting LMP values efficiently.

C.-Y. Hsiao

Papers

Locational marginal price forecasting in deregulated electricity markets using artificial intelligence