This paper introduces a new integrated framework for short-term time series forecasting used as decision logic support in Energy Management Systems (EMS). The proposed framework defines data preprocessing, forecast model training, and adaptation techniques. For data preprocessing, Gaussian and Kalman filtering were used in offline learning on historical data and online (real-time) forecasting. Optimal parametrisation for both filters was selected based on the forecast accuracy. Next, a multiple-input, single-output forecast model based on the Long Short-Term Memory (LSTM) neural network was tuned for each device. The forecast horizon is single-step, and chaining of predictions was used for longer horizons. Finally, the best model was adapted to the changes in the time series data. A significant drop was observed in the forecast accuracy on the past data after adapting the model to the new data using a naive approach. Due to this drop, a novel sample rehearsal selection strategy for adaptation of the model is proposed and compared to other different strategies. All the techniques are demonstrated based on the examples of forecasting multivariate time series for different household devices: a water heater, a refrigerator and an HVAC device. The use of data preprocessing improved the forecast accuracy by up to 56% compared to no preprocessing. Moreover, by introducing a sample rehearsal, knowledge retention was improved by up to 76.6% compared to the naive approach, and by up to 62% compared to the random selection of samples.

Automatic building reconstruction from laser-scanned data remains a challenging research topic due to buildings’ roof complexity and sparse data. A novel automatic building reconstruction methodology, based on half-spaces and a height jump analysis, is presented in this paper. The proposed methodology is performed in three stages. During the preprocessing stage, the classified input point cloud is clustered by position to obtain building point sets, which are then evaluated to obtain half-spaces and detect height jumps. Half-spaces represent the fundamental shape for generating building models, and their definition is obtained from the corresponding segment of points that describe an individual planar surface. The detection of height jumps is based on a DBSCAN search within a custom search space. During the second stage, the building point sets are divided into sub-buildings in such a way that their roofs do not contain height jumps. The concept of sub-buildings without height jumps is introduced to break down the complex building models with height jumps into smaller parts, where shaping with half-spaces can be applied accurately. Finally, the sub-buildings are reconstructed separately with the corresponding half-spaces and then joined back together to form a complete building model. In the experiments, the methodology’s performance was demonstrated on a large scale and validated on an ISPRS benchmark dataset, where an RMSE of 0.29 m was obtained in terms of the height difference.

This paper aims to automatically reconstruct 3D building models on a large scale using a new approach on the basis of half-spaces, while making no assumptions about the building layout and keeping the number of input parameters to a minimum. The proposed algorithm is performed in two stages. First, the airborne LiDAR data and buildings’ outlines are preprocessed to generate buildings’ base models and the corresponding half-spaces. In the second stage, the half-spaces are analysed and used for shaping the final 3D building model using 3D Boolean operations. In experiments, the proposed algorithm was applied on a large scale, and its’ performance was inspected on a city level and on a single building level. Accurate reconstruction of buildings with various layouts were demonstrated and limitations were identified for large-scale applications. Finally, the proposed algorithm was validated on an ISPRS benchmark dataset, where a RMSE of 1.31 m and completeness of 98.9% were obtained.