Abstract The electrical load forecasting is a fundamental technique for consumer load prediction for utilities. The accurate load forecasting is crucial to design Demand Response (DR) programs in the paradigm of smart grids. Artificial Neural Network (ANN) based techniques have been widely used in recent years and applied to predict the electric load with high accuracy to participate in DR programs for commercial, industrial and residential consumers. This research work is focused on the use and comparison of two ANN-based load forecasting techniques, i.e. Feed-Forward Neural Network (FFNN) and Echo State Network (ESN), on a dataset related to commercial buildings, in view of a possible DR program application. The results of both models are compared based on the load forecasting accuracy through experimental measurements and suitably defined metrics.

中文翻译：

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回声状态网络和前馈神经网络在需求响应程序电力负荷预测中的比较

摘要 电力负荷预测是公用事业用电负荷预测的一项基本技术。准确的负荷预测对于在智能电网范式中设计需求响应 (DR) 程序至关重要。近年来，基于人工神经网络 (ANN) 的技术被广泛应用于预测电力负荷以参与商业、工业和住宅消费者的 DR 计划。鉴于可能的 DR，本研究工作的重点是在与商业建筑相关的数据集上使用和比较两种基于 ANN 的负载预测技术，即前馈神经网络 (FFNN) 和回声状态网络 (ESN)程序应用。