For the utility to plan the resources accurately and balance the electricity supply and demand, accurate and timely forecasting is required. The proliferation of smart meters in the grids has resulted in an explosion of energy datasets. Processing such data is challenging and usually takes a longer time than the requirement of a short-term load forecast. The paper addresses this concern by utilizing parallel computing capabilities to minimize the execution time while maintaining highly accurate load forecasting models. In this paper, a thousand smart meter energy datasets are analyzed to perform day ahead, hourly short-term load forecast (STLF). The paper utilizes multi-processing to enhance the overall execution time of the forecasting models by submitting simultaneous jobs to all the processors available. The paper demonstrates the efficacy of the proposed approach through the choice of machine learning (ML) models, execution time, and scalability. The proposed approach is validated on real energy consumption data collected at distribution transformers’ level in Spanish Electrical Grid. Decision trees have outperformed the other models accomplishing a tradeoff between model accuracy and execution time. The methodology takes only 4 minutes to train 1,000 transformers for an hourly day-ahead forecast of (~24 million records) utilizing 32 processors.

中文翻译：

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基于多处理的机器学习算法对配电系统负荷预测的敏感性分析

为了使公用事业能够准确地计划资源并平衡电力供需，需要准确而及时的预测。智能电表在网格中的扩散导致能量数据集的爆炸式增长。处理此类数据具有挑战性，通常比短期负荷预测所需的时间更长。本文通过利用并行计算功能来最大程度地减少执行时间，同时保持高度准确的负载预测模型来解决此问题。在本文中，对一千个智能电表能源数据集进行了分析，以执行提前一天的每小时短期负荷预测（STLF）。本文通过向所有可用处理器同时提交作业，利用多重处理来提高预测模型的总体执行时间。本文通过选择机器学习（ML）模型，执行时间和可伸缩性来论证该方法的有效性。该方法在西班牙电网的配电变压器一级收集的实际能耗数据中得到了验证。决策树的性能优于其他模型，可以在模型准确性和执行时间之间进行权衡。该方法仅用4分钟即可培训32个处理器，对1,000台变压器进行每小时一次的日前预测（约2,400万条记录）。决策树的性能优于其他模型，可以在模型准确性和执行时间之间进行权衡。该方法仅用4分钟即可培训32个处理器，对1,000台变压器进行每小时一次的日前预测（约2,400万条记录）。决策树的性能优于其他模型，可以在模型准确性和执行时间之间进行权衡。该方法仅用4分钟即可培训32个处理器，对1,000台变压器进行每小时一次的日前预测（约2,400万条记录）。