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Input-State-Output Representations of Concatenated 2D Convolutional Codes Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Family Houses Energy Consumption Forecast Tools for Smart Grid Management

Authors : F. Rodrigues, C. Cardeira, J. M. F. Calado, R. Melício

Published in: CONTROLO 2016

Publisher: Springer International Publishing

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Abstract

This paper presents a short term (ST) load forecast (FC) using Artificial Neural Networks (ANNs) or Generalized Reduced Gradient (GRG). Despite the apparent natural unforeseeable behavior of humans, electricity consumption (EC) of a family home can be forecast with some accuracy, similarly to what the electric utilities can do to an agglomerate of family houses. In an existing electric grid, it is important to understand and forecast family house daily or hourly EC with a reliable model for EC and load profile (PF). Demand side management (DSM) programs required this information to adequate the PF of energy load diagram to Electric Generation (EG). In the ST, for load FC model, ANNs were used, taking data from a EC records database. The results show that ANNs or GRG provide a reliable model for FC family house EC and load PF. The use of smart devices such as Cyber-Physical Systems (CPS) for monitoring, gathering and computing a database, improves the FC quality for the next hours, which is a strong tool for Demand Response (DR) and DSM.

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Literature
1.
Sofana, R.S., Ramesh, V.: A demand response modeling for residential consumers in smart grid environment using game theory based energy scheduling algorithm. Ain Shams Eng. J. (2016)
2.
Rodrigues, F., Cardeira, C., Calado, J.M.F., Melício, R.: Energy household forecast with ANN for demand response and demand side management. Renew. Energy Power Qual. J.-ICREPQ 14, 1–4 (2016)
3.
Macedo, M.N.Q., Galo, J.J.M., de Almeida, L.A.L., de Lima, A.C.: Demand side management using artificial neural networks in a smart grid environment. Renew. Sustain. Energy Rev. 41, 128–133 (2015)CrossRef
4.
Rodrigues, F., Cardeira, C., Calado, J.M.F.: The daily and hourly energy consumption and load forecasting using artificial neural network method: a case study using a set of 93 households in Portugal. Energy Procedia 62, 220–229 (2014)CrossRef
5.
Yang, I.H., Kim, K.W.: Prediction of the time of room air temperature descending for heating systems in buildings. Build. Environ. 39(1), 19–29 (2004)CrossRef
6.
Sidler, O.: Demand side management. End-use metering campaign in 400 households of the European Community. SAVE Programme, Project EURECO. Commission of the European Communities: France (2002)
7.
Blaabjerg, F., Ionel, M.: Renewable energy devices and systems–state-of-the-art technology, research and development, challenges and future trends. Electr. Power Compon. Syst. 43(12), 1319–1328 (2015)CrossRef
8.
Viegas, J.L., Vieira, S.M., Melicio, R., Mendes, V.M.F., Sousa, J.M.C.: Classification of new electricity customers based on surveys and smart metering data. Energy 107, 804–817 (2016)CrossRef
9.
Ramos, C., Vale, Z., Faria, L.: Cyber-physical intelligence in the context of power systems. Future Gener. Inf. Technol. 7105, 19–29 (2011)CrossRef
10.
Faria, L., Silva, A., Ramos, C., Vale, Z., Marques, A.: Intelligent behavior in a cyber-ambient training system for control center operators. Proc. ISAP 1–6 (2011)
11.
Ying, S.: Foundations for innovation in cyber-physical systems. Workshop Report, Energetics Incorporated, Columbia, Maryland, US (2013)
12.
Seixas, M., Melício, R., Mendes, V.M.F.: Simulation of rectifier voltage malfunction on OWECS, four-level converter, HVDC light link: smart grid context tool. Energy Convers. Manag. 97, 140–153 (2015)CrossRef
13.
Hippert, H., Pedreira, C., Souza, R.: Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans. Power Syst. 16(1), 44–55 (2001)CrossRef
14.
Carpinteiro, O., Reis, A., Silva, A.: A hierarchical neural model in short-term load forecasting. Appl. Soft Comput. 4(4), 405–412 (2004)CrossRef
15.
Gross, G., Galiana, F.: Short-term load forecasting. Proc. IEEE 75(12), 1558–1573 (1987)CrossRef
16.
Taylor, J.: Short-term load forecasting with exponentially weighted methods. IEEE Trans. Power Syst. 27(1), 458–464 (2012)CrossRef
17.
Feinberg, E., Genethliou, D.: Load forecasting. Applied mathematics for restructured electric power systems. Springer US:269–285 (2005)
18.
Viegas, J.L., Vieira, S.M., Melicio, R., Mendes, V.M.F., Sousa, J.M.C.: GA-ANN short-term electricity load forecasting. In: Camarinha-Matos, L.M., Falcão, A.J., Vafaei, N., Najdi, S. (eds.) 7th IFIP AICT, Technological Innovation for Cyber-Physical Systems, vol. 470, pp. 485–493. Springer (2016)
19.
Saini, L., Soni, M.: Artificial neural network based peak load forecasting using Levenberg-Marquardt and quasi-Newton methods. IEE Proc.-Gener. Transm. Distrib. 149(5), 578–584 (2002)CrossRef
20.
Holderbaum, W., Canart, R., Borne, P.: Artificial neural networks application to boolean input systems control. Stud. Inf. Control 8, 107–120 (1999)
21.
Zebulum, R.S., Vellasco, M., Guedes, K., Pacheco, M.A.: Short-term load forecasting using neural nets. In: Natural to Artificial Neural Computation, pp. 1001–1008 (1995)
22.
Luenberger, D.G., Ye, Y.: Linear and nonlinear programming. Springer, Stanford, USA (2008)MATH
23.
Lasdon, L.S., Waren, A.D., Jain, A., Ratner, M.: Design and testing of a generalized reduced gradient code for nonlinear programming. ACM Trans. Math. Softw. 4(1), 34–50 (1978)CrossRefMATH
Metadata
Title
Family Houses Energy Consumption Forecast Tools for Smart Grid Management
Authors
F. Rodrigues
C. Cardeira
J. M. F. Calado
R. Melício
Copyright Year
2017
Book
CONTROLO 2016

Print ISBN: 978-3-319-43670-8

Electronic ISBN: 978-3-319-43671-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-43671-5_58