A novel multi-energy load forecasting method based on building flexibility feature recognition technology and multi-task learning model integrating LSTM

Abstract

Accurate multi-energy load forecasting is prerequisite for achieving balance between supply and demand in building energy system. The continuous development of building flexibility control techniques has led to increased complexity and variability in characteristics of multi-energy flexibility loads under application of various building flexibility control strategies. Existing load forecasting methods lack the ability to recognize the flexibility features and face challenges in considering complex coupling relationships and variable characteristics of multi-energy flexibility loads. To address this gap, we propose a multi-energy flexibility load forecasting method, denoted as (BFFR-MTL-LSTM), which incorporates building flexibility feature recognition (BFFR) and multi-task learning (MTL) model utilizing Long Short-Term Memory (LSTM) neural networks as the shared layer. A novel ToU-K-means method combining ToU with K-means algorithm is proposed firstly as BFFR technique to accurately recognize the flexibility features under various energy consumption patterns. The forecasting accuracy of proposed method is validated using actual operational data under various energy consumption patterns with average R2 of 0.973. Additionally, through comparisons with numerous existing models, the proposed method demonstrates a forecasting accuracy improvement ranging from 33 % to 47 %. This validation supports that the proposed method adeptly recognizes energy consumption patterns, resulting in more accurate forecasts of multi-energy flexibility loads.