Optimizing Energy Efficiency in Biotechnology Facilities: Short-Term Electricity Load Forecasting with Adaptive Deep Residual Neural Networks

Abstract

Efficient energy management is crucial for the biotechnology sector, where research facilities, pharmaceutical production, and bioprocessing plants rely on stable and cost-effective electricity supply. Accurate short-term electricity load forecasting can optimize energy consumption, reduce operational costs, and enhance sustainability in biotech infrastructure. This study presents a novel forecasting model tailored for biotechnology facilities, integrating temporal awareness and adaptive deep residual neural networks to improve load prediction accuracy. The proposed method encodes time using periodicity-based cosine and sine transformations and incorporates future time as an input variable, enhancing the model’s ability to recognize temporal dependencies. To further improve forecasting precision, adaptive convolutional mechanisms are introduced, allowing convolutional kernels to dynamically adjust their receptive fields, thereby extracting more significant energy consumption patterns. The proposed approach is validated using real-world datasets from the Australian electricity market, demonstrating its applicability to high-energy-demand sectors, including biotechnology. Experimental results show that the method outperforms traditional machine learning models, achieving a 22.9%, 31.2%, and 23.9% reduction in mean squared error compared to convolutional neural networks, long short-term memory networks, and standard residual neural networks, respectively, in 24-hour load forecasting. These findings highlight the potential of AI-driven forecasting models in optimizing energy consumption for biotechnology operations, contributing to cost savings, sustainability, and improved grid stability in biotech infrastructure. This research paves the way for advanced energy-efficient solutions in the biotechnology sector by integrating artificial intelligence and deep learning techniques into sustainable energy management strategies.