Probabilistic Wind Power and Electricity Load Forecasting with Echo State Networks

With the introduction of distributed generation and the establishment of smart grids, several new challenges in energy analytics arose. These challenges can be solved with a specific type of recurrent neural networks called echo state networks, which can handle the combination of both weather and power consumption or production depending on the dataset to make predictions. Echo state networks are particularly suitable for time series forecasting tasks. Having accurate energy forecasts is paramount to assure grid operation and power provision remains reliable during peak hours when the consumption is high. The majority of load forecasting algorithms do not produce prediction intervals with coverage guarantees but rather produce simple point estimates. Information about uncer- tainty and prediction intervals is rarely useless. It helps grid operators change strategies for configuring the grid from conservative to risk-based ones and assess the reliability of operations. A popular way of producing prediction intervals in regression tasks is by applying Bayesian regression as the regression algorithm. As Bayesian regression is done by sampling, it nat- urally lends itself to generating intervals. However, Bayesian regression is not guaranteed to satisfy the designed coverage level for finite samples. This thesis aims to modify the traditional echo state network model to produce marginally valid and calibrated prediction intervals. This is done by replacing the standard linear regression method with Bayesian linear regression while simultaneously reducing the di- mensions to speed up the computation times. Afterward, a novel calibration technique for time series forecasting is applied in order to obtain said valid prediction intervals. The experiments are conducted using three different time series, two of them being a time series of electricity load. One is univariate, and the other is bivariate. The third time series is a wind power production time series. The proposed method showed promising results for all three datasets while significantly reducing computation times in the sampling ste

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces

Probabilistic load forecasting with Reservoir Computing

Some applications of deep learning require not only to provide accurate results but also to quantify the amount of confidence in their prediction. The management of an electric power grid is one of these cases: to avoid risky scenarios, decision-makers need both precise and reliable forecasts of, for example, power loads. For this reason, point forecasts are not enough hence it is necessary to adopt methods that provide an uncertainty quantification. This work focuses on reservoir computing as the core time series forecasting method, due to its computational efficiency and effectiveness in predicting time series. While the RC literature mostly focused on point forecasting, this work explores the compatibility of some popular uncertainty quantification methods with the reservoir setting. Both Bayesian and deterministic approaches to uncertainty assessment are evaluated and compared in terms of their prediction accuracy, computational resource efficiency and reliability of the estimated uncertainty, based on a set of carefully chosen performance metrics

Prediction of the Italian electricity price for smart grid applications

In this paper we address the problem of one day-ahead hourly electricity price forecast for smart grid applications. To this aim, we investigate the application of a number of predictive models for time-series, including methods based on empirical strategies frequently adopted in the smart grid community, Kalman Filters and Echo State Networks (ESNs). The considered methods have been suitably modified to address the electricity price forecast problem. Strategies based on daily re-adaptation of models’ parameters are taken into consideration as well. The predictive performance achieved by the considered models is assessed, and the methods are compared among each other on recent real data from the Italian electricity market. As a result of the comparison over three years data, ESN methods appear to provide the most accurate price predictions, which could imply significant economic savings in many smart grid activities, such as switching on power plants to support power generation from renewable sources, electric vehicle recharging or usage of household appliances

Neural network for estimating and compensating the nonlinear characteristics of nonstationary complex systems

Issued as final reportNational Science Foundation (U.S