Supervisory control design for microgrids energy management optimization based on renewable generation and consumption forecasting

Solar-based electricity production has become an essential part of the general energy production in the recent years with the will to use more renewable sources. The one issue that appears is the uncertainty of the solar irradiation. It is then more complicated to predict the energy generated in the future times. The Energy Management System used on the grid schedules the energy exchanges between the devices based on the prediction of the state of the system in the next time interval. The Model Predictive Control forecasts the power produced as well as that of the energy demand from the load and defines the state of the system. In order to minimize the corresponding cost function, this forecast should be as accurate as possible, with the minimum prediction error. To address these forecasting needs, we will extract some data from a database using an algorithm directly connected to the server. And we will compute the remaining values using an accurate forecasting method, the Simple Average. Then, for this information to be even more precise, we use the Rolling Horizon approach, that enables a regular updating of the forecast. Simulation results and experiments confirm the influence of some parameters on the prediction error and hence on the cost function

A Time Series Dimensionality Reduction Method for Maximum Deviation Reduction and Similarity Search

Similarity search over time series is essential in many applications. However, it may cause a “dimensionality curse” due to the high dimensionality of time series. Various dimensionality reduction methods have been developed. Some of them sacrifice max deviation to get a faster dimensionality reduction, such as equal-length segment dimensionality reduction methods: Piecewise Linear Approximation (PLA), Piecewise Aggregate Approximation (PAA), Chebyshev Polynomials (CHEBY), Piecewise Aggregate Approximation Lagrangian Multipliers (PAALM) and Symbolic Aggregate Approximation (SAX). Some sacrifice dimensionality reduction time for the smallest max deviation, such as adaptive-length segment dimensionality reduction methods: Adaptive Piecewise Linear Approximation (APLA) and Adaptive Piecewise Constant Approximation (APCA). APLA uses a guaranteed upper bound for the best max deviation with slow dimensionality reduction time. We investigate the existing basic dimensionality reduction techniques for time series data. We point out the limitations of the existing dimensionality reduction techniques and evaluate the dimensionality reduction techniques on real-life datasets. We also conduct preliminary research and make some improvements to the existing dimensionality reduction techniques. Our experimental results on several datasets compare and verify the efficiency and effectiveness of the existing techniques, including several dimensionality reduction techniques, one index-building method, and two k-Nearest Neighbours (k-NN) search methods. We propose an adaptive-length segment dimensionality reduction method called Self Adaptive Piecewise Linear Approximation (SAPLA). It consists of 1) initialization, 2) split & merge iteration, and 3) segment endpoint movement iteration. Increment area, reconstruction area, conditional upper bound and several equations are applied to prune redundant computations. The experiments show this method can speed up about n (time series length) times than APLA when reducing the dimensionality of the original time series with minor max deviation loss. We propose a lower bound distance measure between two time series to guarantee no false dismissals and tightness for adaptive-length segment dimensionality reduction methods. When mapping time series into a tree index, we propose Distance Based Covering with Convex Hull Tree (DBCH-tree) and split node and pick branch by using the proposed lower bounding distance, not waste area. Our experiments show that DBCH-tree helps improve k-NN search over time series using dimensionality reduction methods