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• A recent literature review of attempts on various types of Energy Load Forecasting (short/mid/long-term). • A novel one step-ahead forecasting methodology, fine-tuned for predicting short-term energy loads. • Performance analysis, implementing various combinations of ensemble and prediction algorithms evaluated with widely used metrics (RMSE, MAPE and SMAPE). Energy sector stakeholders, such as Distribution System Operators (DSO) or Aggregators take advantage of improved forecasting methods. Increased forecasting accuracy facilitates handling energy imbalances between generation and consumption. It also supports Smart Grid framework processes, such as Demand-Side or Demand Response Management (DRM). This paper presents a novel approach for One-Step-Ahead Energy Load Forecasting (OSA-ELF), considering several techniques. It utilizes historical data from a state-of-the-art nearly Zero Energy Building (nZEB) smart home, performing multiple tests for improved ELF. It focuses on OSA aspects of ELF, yet it can be utilized regardless of the time resolution. It predicts the "next step" value, regardless of the step's duration (15-minutes, one-hour, one-day etc.) with high accuracy, and can be used for a wide variety of forecasting applications. To that end, fine-tuned ensemble methods and forecasting algorithms were utilized for experimenting with short term ELF. Forecasting evaluation produced good results with regards to popular accuracy (MAPE, SMAPE and RMSE) and an Execution Time (ET) metrics. [ABSTRACT FROM AUTHOR]