Combination of Kohonen SOM and Multi- layer Perceptron for modeling and forecasting the energy needs of a Solar House

D.P. Iracleous U.H .

G. Kathreptis Technological Educational Institute of Piraeus

Tel 210 5381304

T.I. Xanthopoulos U.H. University of Hertfordshire

Tel. 210 5381304

Abstract

Neural Networks are used with success to model non-linear systems, as this of a Solar House. Multi- layer Perceptron is used in the majority of papers for forecasting the energy that is required in a Solar House. In this paper the energy behavior of a Solar House is examined, to perform on-line peak power forecast. ΝΝ Kohonen is used to order the energy and meteorological facts, classify them in similar complexes and after that, feed them in an MLP, to achieve faster and more precise forecast of energy demands for a Solar House.

1. Introduction

Artificial Neural Networks (ANN) has been widely used for a range of applications in the area of energy modelling. The literature has demonstrate their superior capability over-conventional methods, such as times series or regression, their main advantage being the high potential to model non-linear processes, such as utility loads or individual buildings energy consumption.

The application of the ANN methodology to the problem of short term load forecasting for electric power utilities has received much of the attention and excellent results have been achieved in real applications [1–5].

A combined approach based on Kohonen and Multi layer neural networks for the electric energy demand forecasting of a Solar House with a prediction time of 24 h is illustrated.

1.1 Kohonen Neural Networks

The Kohonen network is formed by a single layered neural network. The data are presented to the input and the output neurons are organized with a simple neighbourhood structure. Each neuron is associated with a reference vector (the weight vector), and each data point is “mapped” to the neuron with the “closest” (in the sense of the Euclidean distance) reference vector. In the process of running the algorithm, each data point acts as a training sample which directs the movement of the reference vectors towards the value of the data of this sample. The vectors associated with neurons, called weights, change during the learning process and tend to the characteristic values of the distribution of the input data. The characteristic value of one cluster can be intuitively understood as the typical value of the data in the cluster and will be defined more precisely in the next subsections. At the end of the process the set of input data is partitioned into disjoint sets (the clusters) and the weight associated with each neuron is the characteristic value of the cluster associated with the neuron in the one-dimensional case, which is the case of interest to us. We limit our analysis to this case because the condition of convergence of the algorithm is easier to check, the cluster of the partitions are easier to visualize and it is not difficult to compare the behaviour of the genes in the clusters corresponding to the different biological conditions. Each neuron individuates one cluster, and the physical or biological entities with measure values belonging to the same cluster are considered to be involved in the same cellular process. Thus the genes with expressions belonging to the same cluster might be functionally related.

The following points show the main properties which make the Kohonen network useful for clustering:

i) Low dimension of the network and its simple structure.

ii) Simple representation of clusters by means of vectors associated with each neuron.

iii) Topology of the input data set is somehow mapped in the topology of the weights of the network.

iv) Learning is unsupervised.

v) Self-organized property.

Points i and ii are simple to understand. Point iii, means that neighbouring neurons have weight vectors not very different from each other. Point iv, means that the reasoned to have an external constraint to drive the weights towards their right values beside the input to the network and that the learning process finds by itself the right topology and the right values. This holds only if the learning process with which the network is constructed converges.

The self-organization is formulated in the current literature referring to some universality of the structure of the network for a given data set. It is connected to point iii and is also a consequence of a.e. convergence or of the convergence of the average of the weights over many different learning processes.

Figure 1

Kohonen Self – Organising Map (SOM)

1.2. Feed Forward Neural networks as non-linear regression models

Feed forward artificial neural networks, is a class of flexible and widely applied models, used to find the relation between the input and the output variables. The problem can be stated as finding a function f (F: Rd !R) such as to obtain an estimate of the output values y from the input values x. The neural network approach of performing prediction is to induce this function in a standard multilayer perceptron (MLP).

Figure 2

Standard Multi Layer Perceptron

Many studies have pointed out the overwhelming sensitivity of electricity consumption to weather variables, in particular focusing attention on the forecast limited to 24 h ahead.

Recent research activities have also focused on the impacts of climatic changes both on supply (studies about the potential impact of climate change on renewable energy resources, such as wind power and hydroelectric power) and demand (studies about electricity demand and natural gas demand correlation with weather variability) for energy. Weather sensitivity has also been examined in order to correlate electricity consumptions to the increases in market saturation of air conditioning induced by long term climatic changes.

Furthermore, it should be noticed that in order to model energy use, environmental variables such as ambient temperature, solar radiation or wind speed are often used, as weather strongly affects the energy consumption of buildings.

However, for predictive control applications where the input data at the target time are needed, i.e. to get a prediction 24 h ahead, a pre-process is required before the prediction can be made and usually is based on first predicting the weather data for the next day, either using a predicting methodology, or by directly linked to weather reports, say via the Internet.

2. Our proposal

The combination of Kohonen SOM and Multi Layer Perceptron is our proposal in order to manage the energy use in a Solar House.

In our scenario, a family of four persons is resident of a 70m2 house. The location of the house is a very important matter that will not be discussed in this paper.

For standard house electrical equipment, that covers the needs of the residents (refrigerator, cooker, electric bulbs and entertainment electrical appliances that are allowed to consume pre- determined monthly amount of electrical energy every month)

θ out
Η out
θ in
H in
Day
Month
Sun Flux

Figure 3

θ out external temperature

H out external humidity

θ in internal temperature

Day

Month

Sun flux measurement outside the Solar House

Kohonen SOM will use these parameters to extract and classify the information needed to feed the MLP in order to achieve thermal stability inside the Solar House.

It is our belief that using Kohonen SOM in combination with the MLP will increase the response and the accuracy of the system that controls the energy use in the Solar House.

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