In this paper, an emerging state-of-the-art machine intelligence technique called the Hierarchical Temporal Memory (HTM) is applied to the task of short-term load forecasting (STLF). A HTM Spatial Pooler (HTM-SP) stage is used to continually form sparse distributed representations (SDRs) from a univariate load time series data, a temporal aggregator is used to transform the SDRs into a sequential bivariate representation space and an overlap classifier makes temporal classifications from the bivariate SDRs through time. The comparative performance of HTM on several daily electrical load time series data including the Eunite competition dataset and the Polish power system dataset from 2002 to 2004 are presented. The robustness performance of HTM is also further validated using hourly load data from three more recent electricity markets. The results obtained from experimenting with the Eunite and Polish dataset indicated that HTM will perform better than the existing techniques reported in the literature. In general, the robustness test also shows that the error distribution performance of the proposed HTM technique is positively skewed for most of the years considered and with kurtosis values mostly lower than a base value of 3 indicating a reasonable level of outlier rejections.

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In this paper, we present an open-source software tool 'ABC-PLOSS', which is developed for use in optimisation processes. Path-loss optimisation deals with searching for the best set of operator-specific parameters in telecommunication that gives the least cost of operation. It is a primary issue that challenges mobile communication operators, particularly the global system mobile (GSM) operators in tuning mobile-base station networks for efficient and reliable operation. The tool uses a sequential processor architecture based on a swarm intelligence algorithm called artificial bee colony (ABC) and the cost-231 Hata path-loss model as cost function for path-loss minimisation (PLM). Using the ABC-PLOSS framework, the ABC algorithm is compared with two other existing and popular artificial intelligent (AI) algorithms called the genetic algorithm (GA) and particle swarm optimisation (PSO). Results of simulation studies show that this tool is indeed useful as it gives a competitive or lower path-loss estimate when compared with conventional techniques. It also shows that it is possible for the ABC to attain an estimated seven-fold and twofold path-loss improvement over the GA and the PSO techniques respectively.

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