Energy load forecasting: one-step ahead hybrid model utilizing ensembling

Abstract

In the light of the adverse effects of climate change, data analysis and Machine Learning (ML) techniques can provide accurate forecasts, which enable efficient scheduling and operation of energy usage. Especially in the built environment, Energy Load Forecasting (ELF) enables Distribution System Operators or Aggregators to accurately predict the energy demand and generation trade-offs. This paper focuses on developing and comparing predictive algorithms based on historical data from a near Zero Energy Building. This involves energy load, as well as temperature data, which are used to develop and evaluate various base ML algorithms and methodologies, including Artificial Neural Networks and Decision-trees, as well as their combination. Each algorithm is fine-tuned and tested, accounting for the unique data characteristics, such as the presence of photovoltaics, in order to produce a robust approach for One-Step-Ahead ELF. To this end, a novel hybrid model utilizing ensemble methods was developed. It combines multiple base ML algorithms the outputs of which are utilized to train a meta-model voting regressor. This hybrid model acts as a normalizer for any new data input. An experimental comparison of the model against unseen data and other ensemble approaches, showed promising forecasting results (mean absolute percentage error = 5.39%), particularly compared to the base algorithms.

Energy Forecasting: A Comprehensive Review of Techniques and Technologies

“…It was also highlighted that ensemble methods, leveraging the strengths of multiple forecasting models, have shown significant promise in improving prediction accuracy. Techniques ranging from the BEM to the GEM and dynamically weighted ensembles highlight the diversity and potential of ensemble approaches in energy forecasting [111][112][113][114][115][116][117][118]. These methods, through the strategic combination and weighting of models, aim to reduce bias and variance, presenting a comprehensive approach to tackling the intricacies of energy forecasting challenges.…”

“…Other ensembles include ensemble models where NN learners train on the output of TBMs [116] for one-step-ahead ELF or dynamically weighted ensembles utilising a combination of RNNs and TBMs for multistep-ahead EGF [21]. In addition to the traditional time-series methods, a Zero-Inflated (ZI) regression approach was also used [117,118] on datasets with a very high number of zero values for their target parameter.…”

“…The link between independent variables (input) and the projected value (output) is dynamic, as time-series data frequently show trends and seasonal patterns. Choosing an acceptable time-lag value (i.e., the number of prior observations to examine) must be carefully considered to identify the precise time instances where the values of independent variables are strongly associated [116].…”

Integrating Blockchain in Smart Grids for Enhanced Demand Response: Challenges, Strategies, and Future Directions

“…Technological advancements in data analytics, the Internet of Things (IoT), and smart devices have enhanced DR's capability for real-time energy usage control, making it an essential facet of daily energy management [14]. Another key aspect of DR involves supervised learning data mining procedures like one [15] or multi-step [16] load or generation [17] forecasting when sometimes modeling information as complex information networks [18].…”

Urban Traffic Congestion Prediction: A Multi-Step Approach Utilizing Sensor Data and Weather Information

“…Numerous recent studies have investigated the interaction of the IoT infrastructure and ML to realize a data-driven intelligent environment. Aspects such as smart healthcare [11], energy generation [12] or energy consumption predictions [13], and the energy grid [14] are examples of SCs that enable data mining tasks. Major considerations include intelligent traffic signals [15], traffic jam predictions, and management [16].…”