Energy budgeting and control: A new approach for an industrial plant

International Journal of Energy Sector Management

Volumn 3, Issue 2, 2009, Pages 131-156

  Cesarotti, Vittorio ^a   Di Silvio, Bruna ^a   Introna, Vito ^a  

^a UNIVERSITY OF ROME TOR VERGATA   (Italy)

Author keywords

Budgetary control; Electricity; Energy consumption; Energy industry; Forecasting

Indexed keywords

BUDGETARY CONTROL; CLIMATIC VARIATION; CONTINUOUS CONTROL; CONTINUOUS IMPROVEMENTS; DESIGN/METHODOLOGY/APPROACH; ELECTRICAL ENERGY COSTS; ENERGY BUDGETS; ENERGY CONSUMPTION; ENERGY CONSUMPTION AND COST; ENERGY INDUSTRY; HIGH CONFIDENCE; INDUSTRIAL CONSUMERS; MANAGEMENT TECHNIQUES; NEW APPROACHES; PRACTICAL GUIDE; PRODUCTION PLANS; PRODUCTION VOLUMES; SECOND LEVEL; STAGE ANALYSIS;

BUDGET CONTROL; GAGES; INDUSTRIAL PLANTS; PHILOSOPHICAL ASPECTS; PLANNING; PRODUCTION CONTROL; PROJECT MANAGEMENT;

ELECTRIC LOAD FORECASTING;

EID: 67949095955     PISSN: 17506220     EISSN: None     Source Type: Journal    
DOI: 10.1108/17506220910970551     Document Type: Article

References (14)

1. Amara, R. and Salanik, G. (1972), Forecasting: from conjectural art toward science, Technological Forecasting and Social Change, Vol. 3 No. 3, pp. 415-26.
2. Bezdek, R.H. and Wendling, R.M. (2002), A half-century of long-range energy forecasts: errors made, lessons learned and implications for forecasting, Journal of Fusion Energy, Vol. 21 Nos 3/4, pp. 155-72.
3. Bielinska, E.M. and Nabaglo, I.E. (1994), Comparison of different methods of bilinear time series prediction, Proceedings of the Third IEEE Conference on Control Applications, Volume 3, August 24-26, pp. 1835-9.
4. Capehart, B.L., Turner, W.C. and Kennedy, W.J. (1997), Guide to Energy Management, Fairmont Press, Lilburn, GA.
5. Cesarotti, V., Di Silvio, B. and Introna, V. (2007), Evaluation of electricity rates through characterization and forecasting of energy consumption: A case study of an Italian industrial eligible customer, International Journal of Energy Sector Management, Vol. 1 No. 4, pp. 390-412.
6. Da Silva, A.P., Rodrigues, U.P., Rocha Reis, A.J. and Moulin, L.S. (2001), A neural network based short term demand forecaster, Proceedings of the IEEE Power Tech Conference, Portugal, Vol. 2 No. 2, pp. 6-12.
7. Deming, W.E. (1986), Out of the Crisis, MIT Press, Cambridge, MA.
8. Energy Efficiency Enquiries Bureau (1998), Monitoring and Targeting in Small and Medium-sized Companies, Good Practice Guide No.125, Carbon Trust.
9. Ghiassi, M., Zimbra, D.K. and Saidane, H. (2006), Medium term system load forecasting with a dynamic artificial neural network model, Electric Power Systems Research, Vol. 76 No. 5, pp. 302-16.
10. Levine, D.M., Krehbiel, T.C. and Berenson, M.L. (2005), Basic Business Statistics: Concepts and Applications, Prentice-Hall, Englewood Cliffs, NJ.
11. Montgomery, D.C. (2005), Design and Analysis of Experiment, Wiley, New York, NY.
12. PMI (2004), A Guide to the Project Management Body of Knowledge, PMBOK Guides, Project Management Institute.
13. Turner, W.C. (2001), Energy Management Handbook, Fairmont Press, Lilburn, GA.
14. Yalcinoz, T.U. and Eminoglu, T. (2005), Short term and medium term power distribution load forecasting by neural networks, Energy Conversion and Management, Vol. 46 Nos 9/10, pp. 1393-405.