Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation

The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been proposed. On the other hand, short-term generation forecasting models for photovoltaic plants have been more recently developed and the MAPEs are in general still far from those achieved from load forecasting models. The aim of this paper is to propose a methodology that could help power systems or aggregators to make up for the lack of accuracy of the current forecasting methods when predicting renewable energy generation. The proposed methodology is carried out in three consecutive steps: (1) short-term forecasting of energy consumption and renewable generation; (2) classification of daily pattern for the renewable generation data using Dynamic Time Warping; (3) application of Demand Response strategies using Physically Based Load Models. Real data from a small town in Spain were used to illustrate the performance and efficiency of the proposed procedure.

[1]  Hong-Tzer Yang,et al.  Identification of ARMAX model for short term load forecasting: an evolutionary programming approach , 1995 .

[2]  Grzegorz Dudek,et al.  Short-Term Load Forecasting Using Random Forests , 2014, IEEE Conf. on Intelligent Systems.

[3]  Sergio Valero-Verdú,et al.  Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response , 2018 .

[4]  Haixiang Guo,et al.  An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting , 2017 .

[5]  Michael Zeifman,et al.  Nonintrusive appliance load monitoring: Review and outlook , 2011, IEEE Transactions on Consumer Electronics.

[6]  P. McSharry,et al.  A comparison of univariate methods for forecasting electricity demand up to a day ahead , 2006 .

[7]  R. Urraca,et al.  Review of photovoltaic power forecasting , 2016 .

[8]  Souhaib Ben Taieb,et al.  A gradient boosting approach to the Kaggle load forecasting competition , 2014 .

[9]  Silja Meyer-Nieberg,et al.  Electric load forecasting methods: Tools for decision making , 2009, Eur. J. Oper. Res..

[10]  W. Charytoniuk,et al.  Nonparametric regression based short-term load forecasting , 1998 .

[11]  Giorgio Graditi,et al.  Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production , 2017 .

[12]  Pablo Montero,et al.  TSclust: An R Package for Time Series Clustering , 2014 .

[13]  Gwo-Ching Liao,et al.  Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting , 2006, IEEE Transactions on Evolutionary Computation.

[14]  Andrés Sanz-García,et al.  GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace , 2015, Appl. Soft Comput..

[15]  Clifford W. Hansen,et al.  Evaluation of Global Horizontal Irradiance to Plane-of-Array Irradiance Models at Locations Across the United States , 2015, IEEE Journal of Photovoltaics.

[16]  Xiaobo Zhang,et al.  Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm , 2017 .

[17]  T. Warren Liao,et al.  Clustering of time series data - a survey , 2005, Pattern Recognit..

[18]  Yan Su,et al.  An ARMAX model for forecasting the power output of a grid connected photovoltaic system , 2014 .

[19]  Jian Chu,et al.  Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A compa , 2011 .

[20]  Jin Wen,et al.  Review of building energy modeling for control and operation , 2014 .

[21]  Jianzhou Wang,et al.  Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting , 2016 .

[22]  Tariq Samad,et al.  Automated Demand Response for Smart Buildings and Microgrids: The State of the Practice and Research Challenges , 2016, Proceedings of the IEEE.

[23]  Chin Kim Gan,et al.  Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS) , 2019, Applied Sciences.

[24]  Ying Wah Teh,et al.  Time-series clustering - A decade review , 2015, Inf. Syst..

[25]  Joaquim Melendez,et al.  Short-term load forecasting in a non-residential building contrasting models and attributes , 2015 .

[26]  F. J. Martinez-de-Pison,et al.  The value of day-ahead forecasting for photovoltaics in the Spanish electricity market , 2017 .

[27]  Kelvin K. W. Yau,et al.  Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks , 2007 .

[28]  Germain Forestier,et al.  Optimizing dynamic time warping’s window width for time series data mining applications , 2018, Data Mining and Knowledge Discovery.

[29]  J. Duffie,et al.  Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation , 1982 .

[30]  Carlos F.M. Coimbra,et al.  Short-term reforecasting of power output from a 48 MWe solar PV plant , 2015 .

[31]  K. Ellegård,et al.  Visualizing energy consumption activities as a tool for making everyday life more sustainable , 2011 .

[32]  J. Friedman.  Stochastic gradient boosting , 2002 .

[33]  Yan Su,et al.  Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines , 2016 .

[34]  Yi-Ming Wei,et al.  Short term electricity load forecasting using a hybrid model , 2018, Energy.

[35]  Hesham K. Alfares,et al.  Electric load forecasting: Literature survey and classification of methods , 2002, Int. J. Syst. Sci..

[36]  Desheng Dash Wu,et al.  Power load forecasting using support vector machine and ant colony optimization , 2010, Expert Syst. Appl..

[37]  Irena Koprinska,et al.  Multi-step forecasting for big data time series based on ensemble learning , 2019, Knowl. Based Syst..

[38]  Yanbo Che,et al.  A short-term photovoltaic power prediction model based on the gradient boost decision tree , 2018 .

[39]  A. Gabaldón,et al.  Integration of Demand Response and Photovoltaic Resources in Residential Segments , 2018 .

[40]  Jun Hu,et al.  Short-Term Load Forecasting With Deep Residual Networks , 2018, IEEE Transactions on Smart Grid.

[41]  A. Gabaldon,et al.  Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees , 2018, Energies.

[42]  Antonio GABALDÓN,et al.  Residential end-uses disaggregation and demand response evaluation using integral transforms , 2017 .