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Time series forecasting is an important predictive methodology which can beapplied to a wide range of problems. Particularly, forecasting the indoortemperature permits an improved utilization of the HVAC (Heating, Ventilatingand Air Conditioning) systems in a home and thus a better energy efficiency.With such purpose the paper describes how to implement an Artificial NeuralNetwork (ANN) algorithm in a low cost system-on-chip to develop an autonomousintelligent wireless sensor network. The present paper uses a Wireless SensorNetworks (WSN) to monitor and forecast the indoor temperature in a smart home,based on low resources and cost microcontroller technology as the 8051MCU. Anon-line learning approach, based on Back-Propagation (BP) algorithm for ANNs,has been developed for real-time time series learning. It performs the modeltraining with every new data that arrive to the system, without saving enormousquantities of data to create a historical database as usual, i.e., withoutprevious knowledge. Consequently to validate the approach a simulation studythrough a Bayesian baseline model have been tested in order to compare with adatabase of a real application aiming to see the performance and accuracy. Thecore of the paper is a new algorithm, based on the BP one, which has beendescribed in detail, and the challenge was how to implement a computationaldemanding algorithm in a simple architecture with very few hardware resources.

Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes