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Open AccessArticle

Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

School of Computing, College of Science, Engineering and Technology, University of South Africa, P.O. Box 392, UNISA 0003 Pretoria, South Africa
Author to whom correspondence should be addressed.
Sustainability 2017, 9(11), 1972;
Received: 29 September 2017 / Revised: 21 October 2017 / Accepted: 22 October 2017 / Published: 28 October 2017
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Users of electronic devices sometimes consume fluctuating amounts of electricity generated from smart-grid infrastructure owned by the government or private investors. However, frequent imbalance is noticed between the demand and supply of electricity, hence effective planning is required to facilitate its distribution among consumers. Such effective planning is stimulated by the need to predict future consumption within a short period. Although several interesting classical techniques have been used for such predictions, they still require improvement for the purpose of reducing significant predictive errors when used for short-term load forecasting. This research develops a near-zero cooperative probabilistic scenario analysis and decision tree (PSA-DT) model to address the lacuna of enormous predictive error faced by the state-of-the-art models. The PSA-DT is based on a probabilistic technique in view of the uncertain nature of electricity consumption, complemented by a DT to reinforce the collaboration of the two techniques. Based on detailed experimental analytics on residential, commercial and industrial data loads, the PSA-DT model outperforms the state-of-the-art models in terms of accuracy to a near-zero error rate. This implies that its deployment for electricity demand planning will be of great benefit to various smart-grid operators and homes. View Full-Text
Keywords: energy; electricity; smart-grid; forecast; smart-home; demand; load; modelling energy; electricity; smart-grid; forecast; smart-home; demand; load; modelling
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Alani, A.Y.; Osunmakinde, I.O. Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes. Sustainability 2017, 9, 1972.

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