Abstract
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.
Highlights
Predicting electricity demand is crucial, since it plays a significant role in the administration, decision-making and demand planning of utility power supply operations [1]
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The critical analysis was conducted as shown in experiments 1 and 2 and the tabular results in Table 4 on the cooperative probabilistic scenario analysis and decision tree (PSA-Decision Tree (DT)) model performance with emphasis on reducing the predictive error that can result in high accuracy for electricity load forecasting in an SG
Summary
Predicting electricity demand is crucial, since it plays a significant role in the administration, decision-making and demand planning of utility power supply operations [1]. Effectiveness and accuracy in terms of extremely reduced forecasting error of a predictive model cannot be overemphasised, as load forecasting guides power grid operations and power station construction planning. Short-term load forecasting (STLF), the generic abbreviation for a model that can predict future load consumption with a lead time of up to a few hours or a few days, has been undergoing constant improvement in the last few decades [3]. Inaccurate load forecasting for effective demand planning remains a difficult and critical challenge [4]. This problem invariably increases the operating costs of electricity suppliers [5]. The Negative (−ve) bars and points shown above the x-axis in Figure 1a–c mean the load forecasting values were low compared to the actual consumption after
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