Sensitivity learning oriented nonmonotonic multi reservoir echo state network for short-term load forecasting

Load forecasting is becoming an important issue day by day for economic generation of power, economic allocation between plants, maintenance scheduling and for system security which involves peak load shaving by power inter change with interconnecting utilities. In this paper, sensitivity learning oriented multi reservoir Echo State Network (ESN) using non monotonic transfer function with optimized structures by particle swarm optimization (PSO) algorithm, are used for short term load forecasting. Load time series of Electric Reliability Council of Texas (ERCOT) control area and Australian Energy Market Operator (AEMO) data are used for benchmarking the proposed method. Sensitivity oriented Linear Learning gives the sensitivities of the sum of squared errors. It has no extra computational cost, because the required information becomes available without having extra calculations. Echo state network parameters are being optimized with well-known Particle swarm optimization technique. Experimental results depicts that the proposed sensitivity oriented non monotonic Echo State network (SNESN) offers superior performance, in terms of mean absolute percentage error (MAPE), in time series prediction and eventually outperform the traditional load forecasting model like ARIMA and modern techniques like Support Vector Machine (SVM) based Genetic algorithm, Wavelet Neural Network and ANN based Fuzzy Network which prove the state of the art.